@Article{info:doi/10.2196/71844, author="Ba, Hongjun and Zhang, Lili and He, Xiufang and Li, Shujuan", title="Knowledge Mapping and Global Trends in Simulation in Medical Education: Bibliometric and Visual Analysis", journal="JMIR Med Educ", year="2025", month="Mar", day="26", volume="11", pages="e71844", keywords="medical education", keywords="simulation-based teaching", keywords="bibliometrics", keywords="visualization analysis", keywords="knowledge mapping", abstract="Background: With the increasing recognition of the importance of simulation-based teaching in medical education, research in this field has developed rapidly. To comprehensively understand the research dynamics and trends in this area, we conducted an analysis of knowledge mapping and global trends. Objective: This study aims to reveal the research hotspots and development trends in the field of simulation-based teaching in medical education from 2004 to 2024 through bibliometric and visualization analyses. Methods: Using CiteSpace and VOSviewer, we conducted bibliometric and visualization analyses of 6743 articles related to simulation-based teaching in medical education, published in core journals from 2004 to 2024. The analysis included publication trends, contributions by countries and institutions, author contributions, keyword co-occurrence and clustering, and keyword bursts. Results: From 2004 to 2008, the number of articles published annually did not exceed 100. However, starting from 2009, the number increased year by year, reaching a peak of 850 articles in 2024, indicating rapid development in this research field. The United States, Canada, the United Kingdom, Australia, and China published the most articles. Harvard University emerged as a research hub with 1799 collaborative links, although the overall collaboration density was low. Among the 6743 core journal articles, a total of 858 authors were involved, with Lars Konge and Adam Dubrowski being the most prolific. However, collaboration density was low, and the collaboration network was relatively dispersed. A total of 812 common keywords were identified, forming 4189 links. The keywords ``medical education,'' ``education,'' and ``simulation'' had the highest frequency of occurrence. Cluster analysis indicated that ``cardiopulmonary resuscitation'' and ``surgical education'' were major research hotspots. From 2004 to 2024, a total of 20 burst keywords were identified, among which ``patient simulation,'' ``randomized controlled trial,'' ``clinical competence,'' and ``deliberate practice'' had high burst strength. In recent years, ``application of simulation in medical education,'' ``3D printing,'' ``augmented reality,'' and ``simulation training'' have become research frontiers. Conclusions: Research on the application of simulation-based teaching in medical education has become a hotspot, with expanding research areas and hotspots. Future research should strengthen interinstitutional collaboration and focus on the application of emerging technologies in simulation-based teaching. ", doi="10.2196/71844", url="https://mededu.jmir.org/2025/1/e71844" } @Article{info:doi/10.2196/64679, author="Grimes, Robert David and Gorski, H. David", title="Quantifying Public Engagement With Science and Malinformation on COVID-19 Vaccines: Cross-Sectional Study", journal="J Med Internet Res", year="2025", month="Mar", day="21", volume="27", pages="e64679", keywords="misinformation", keywords="altmetrics", keywords="disinformation", keywords="malinformation", keywords="public engagement", keywords="medical journals", keywords="medicoscientific", keywords="public health", keywords="altmetric analysis", keywords="comparative analysis", keywords="social media", keywords="Twitter", keywords="vaccine", keywords="digital health", keywords="mHealth", keywords="mobile health", keywords="health informatics", abstract="Background: Medical journals are critical vanguards of research, and previous years have seen increasing public interest in and engagement with medicoscientific findings. How findings propagate and are understood and what harms erroneous claims might cause to public health remain unclear, especially on publicly contentious topics like COVID-19 vaccines. Gauging the engagement of the public with medical science and quantifying propagation patterns of medicoscientific papers are thus important undertakings. In contrast to misinformation and disinformation, which pivot on falsehood, the more nuanced issue of malinformation, where ostensibly true information is presented out of context or selectively curated to cause harm and misconception, has been less researched. As findings and facts can be selectively marshaled to present a misleading picture, it is crucial to consider this issue and its potential ramifications. Objective: This study aims to quantify patterns of public engagement with medical research and the vectors of propagation taken by a high-profile incidence of medical malinformation. Methods: In this work, we undertook an analysis of all altmetric engagements over a decade for 5 leading general-purpose medical journals, constituting approximately 9.8 million engagements with 84,529 papers. We identify and examine the proliferation of sentiment concerning a high-profile publication containing vaccine-negative malinformation. Engagement with this paper, with the highest altmetric score of any paper in an academic journal ever released, was tracked across media outlets worldwide and in social media users on Twitter (subsequently rebranded as X). Vectoring media sources were analyzed, and manual sentiment analysis on high-engagement Twitter shares of the paper was undertaken, contrasted with users' prior vaccine sentiment. Results: Results of this analysis suggested that this COVID-19 scientific malinformation was much more likely to be engaged and amplified with negative by vaccine-negative Twitter accounts than neutral ones (odds ratio 58.2, 95\% CI 9.7-658.0; P<.001), often alluding to the ostensible prestige of medical journals. Malinformation was frequently invoked by conspiracy theory websites and non-news sources (71/181 citations, 39.2\%) on the internet to cast doubt on the efficacy of vaccination, many of whom tended to cite the paper repeatedly (51/181, 28.2\%). Conclusions: Our findings suggest growing public interest in medical science and present evidence that medical and scientific journals need to be aware of not only the potential overt misinformation but also the more insidious impact of malinformation. Also, we discuss how journals and scientific communicators can reduce the influence of malinformation on public understanding. ", doi="10.2196/64679", url="https://www.jmir.org/2025/1/e64679" } @Article{info:doi/10.2196/56692, author="Chien, Shuo-Chen and Yen, Chia-Ming and Chang, Yu-Hung and Chen, Ying-Erh and Liu, Chia-Chun and Hsiao, Yu-Ping and Yang, Ping-Yen and Lin, Hong-Ming and Yang, Tsung-En and Lu, Xing-Hua and Wu, I-Chien and Hsu, Chih-Cheng and Chiou, Hung-Yi and Chung, Ren-Hua", title="Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis", journal="J Med Internet Res", year="2025", month="Mar", day="4", volume="27", pages="e56692", keywords="bibliometric analysis", keywords="Google Trends", keywords="content analysis", keywords="long-term care", keywords="older adults", keywords="artificial intelligence", keywords="Internet of Things", keywords="edge intelligence", abstract="Background: The global aging population poses critical challenges for long-term care (LTC), including workforce shortages, escalating health care costs, and increasing demand for high-quality care. Integrating artificial intelligence (AI), the Internet of Things (IoT), and edge intelligence (EI) offers transformative potential to enhance care quality, improve safety, and streamline operations. However, existing research lacks a comprehensive analysis that synthesizes academic trends, public interest, and deeper insights regarding these technologies. Objective: This study aims to provide a holistic overview of AI, IoT, and EI applications in LTC for older adults through a comprehensive bibliometric analysis, public interest insights from Google Trends, and content analysis of the top-cited research papers. Methods: Bibliometric analysis was conducted using data from Web of Science, PubMed, and Scopus to identify key themes and trends in the field, while Google Trends was used to assess public interest. A content analysis of the top 1\% of most-cited papers provided deeper insights into practical applications. Results: A total of 6378 papers published between 2014 and 2023 were analyzed. The bibliometric analysis revealed that the United States, China, and Canada are leading contributors, with strong thematic overlaps in areas such as dementia care, machine learning, and wearable health monitoring technologies. High correlations were found between academic and public interest, in key topics such as ``long-term care'' ($\tau$=0.89, P<.001) and ``caregiver'' ($\tau$=0.72, P=.004). The content analysis demonstrated that social robots, particularly PARO, significantly improved mood and reduced agitation in patients with dementia. However, limitations, including small sample sizes, short study durations, and a narrow focus on dementia care, were noted. Conclusions: AI, IoT, and EI collectively form a powerful ecosystem in LTC settings, addressing different aspects of care for older adults. Our study suggests that increased international collaboration and the integration of emerging themes such as ``rehabilitation,'' ``stroke,'' and ``mHealth'' are necessary to meet the evolving care needs of this population. Additionally, incorporating high-interest keywords such as ``machine learning,'' ``smart home,'' and ``caregiver'' can enhance discoverability and relevance for both academic and public audiences. Future research should focus on expanding sample sizes, conducting long-term multicenter trials, and exploring broader health conditions beyond dementia, such as frailty and depression. ", doi="10.2196/56692", url="https://www.jmir.org/2025/1/e56692", url="http://www.ncbi.nlm.nih.gov/pubmed/40053718" } @Article{info:doi/10.2196/66286, author="Luo, Jia-Yuan and Deng, Yu-Long and Lu, Shang-Yi and Chen, Si-Yan and He, Rong-Quan and Qin, Di-Yuan and Chi, Bang-Teng and Chen, Gang and Yang, Xia and Peng, Wei", title="Current Status and Future Directions of Ferroptosis Research in Breast Cancer: Bibliometric Analysis", journal="Interact J Med Res", year="2025", month="Feb", day="26", volume="14", pages="e66286", keywords="breast cancer", keywords="ferroptosis", keywords="bibliometric", keywords="malignancy", keywords="cancer studies", keywords="treatment", keywords="bibliometric analysis", keywords="VOSviewer", keywords="China", keywords="United States", keywords="breast carcinoma", keywords="mammary cancer", keywords="strategy", keywords="trends", keywords="bibliography", keywords="review", keywords="disparities", keywords="forecast", keywords="treatment strategies", keywords="advancements", abstract="Background: Ferroptosis, as a novel modality of cell death, holds significant potential in elucidating the pathogenesis and advancing therapeutic strategies for breast cancer. Objective: This study aims to comprehensively analyze current ferroptosis research and future trends, guiding breast cancer research advancements and innovative treatment strategies. Methods: This research used the R package Bibliometrix (Department of Economic and Statistical Sciences at the University of Naples Federico II), VOSviewer (Centre for Science and Technology Studies at Leiden University), and CiteSpace (Drexel University's College of Information Science and Technology), to conduct a bibliometric analysis of 387 papers on breast cancer and ferroptosis from the Web of Science Core Collection. The analysis covers authors, institutions, journals, countries or regions, publication volumes, citations, and keywords. Results: The number of publications related to this field has surged annually, with China and the United States collaborating closely and leading in output. Sun Yat-sen University stands out among the institutions, while the journal Frontiers in Oncology and the author Efferth T contribute significantly to the field. Highly cited papers within the domain primarily focus on the induction of ferroptosis, protein regulation, and comparisons with other modes of cell death, providing a foundation for breast cancer treatment. Keyword analysis highlights the maturity of glutathione peroxidase 4-related research, with breast cancer subtypes emerging as motor themes and the tumor microenvironment, immunotherapy, and prognostic models identified as basic themes. Furthermore, the application of nanoparticles serves as an additional complement to the basic themes. Conclusions: The current research status in the field of ferroptosis and breast cancer primarily focuses on the exploration of relevant theoretical mechanisms, whereas future trends and mechanisms emphasize the investigation of therapeutic strategies, particularly the clinical application of immunotherapy related to the tumor microenvironment. Nanotherapy has demonstrated significant clinical potential in this domain. Future research directions should deepen the exploration in this field and accelerate the clinical translation of research findings to provide new insights and directions for the innovation and development of breast cancer treatment strategies. ", doi="10.2196/66286", url="https://www.i-jmr.org/2025/1/e66286" } @Article{info:doi/10.2196/58227, author="Ivanitskaya, V. Lana and Erzikova, Elina", title="Visualizing YouTube Commenters' Conceptions of the US Health Care System: Semantic Network Analysis Method for Evidence-Based Policy Making", journal="JMIR Infodemiology", year="2025", month="Feb", day="11", volume="5", pages="e58227", keywords="social media", keywords="semantic network", keywords="health system", keywords="health policy", keywords="ideology", keywords="VOSviewer", keywords="health care reform", keywords="health services", keywords="health care workforce", keywords="health insurance", abstract="Background: The challenge of extracting meaningful patterns from the overwhelming noise of social media to guide decision-makers remains largely unresolved. Objective: This study aimed to evaluate the application of a semantic network method for creating an interactive visualization of social media discourse surrounding the US health care system. Methods: Building upon bibliometric approaches to conducting health studies, we repurposed the VOSviewer software program to analyze 179,193 YouTube comments about the US health care system. Using the overlay-enhanced semantic network method, we mapped the contents and structure of the commentary evoked by 53 YouTube videos uploaded in 2014 to 2023 by right-wing, left-wing, and centrist media outlets. The videos included newscasts, full-length documentaries, political satire, and stand-up comedy. We analyzed term co-occurrence network clusters, contextualized with custom-built information layers called overlays, and performed tests of the semantic network's robustness, representativeness, structural relevance, semantic accuracy, and usefulness for decision support. We examined how the comments mentioning 4 health system design concepts---universal health care, Medicare for All, single payer, and socialized medicine---were distributed across the network terms. Results: Grounded in the textual data, the macrolevel network representation unveiled complex discussions about illness and wellness; health services; ideology and society; the politics of health care agendas and reforms, market regulation, and health insurance; the health care workforce; dental care; and wait times. We observed thematic alignment between the network terms, extracted from YouTube comments, and the videos that elicited these comments. Discussions about illness and wellness persisted across time, as well as international comparisons of costs of ambulances, specialist care, prescriptions, and appointment wait times. The international comparisons were linked to commentaries with a higher concentration of British-spelled words, underscoring the global nature of the US health care discussion, which attracted domestic and global YouTube commenters. Shortages of nurses, nurse burnout, and their contributing factors (eg, shift work, nurse-to-patient staffing ratios, and corporate greed) were covered in comments with many likes. Comments about universal health care had much higher use of ideological terms than comments about single-payer health systems. Conclusions: YouTube users addressed issues of societal and policy relevance: social determinants of health, concerns for populations considered vulnerable, health equity, racism, health care quality, and access to essential health services. Versatile and applicable to health policy studies, the method presented and evaluated in our study supports evidence-based decision-making and contextualized understanding of diverse viewpoints. Interactive visualizations can help to uncover large-scale patterns and guide strategic use of analytical resources to perform qualitative research. ", doi="10.2196/58227", url="https://infodemiology.jmir.org/2025/1/e58227", url="http://www.ncbi.nlm.nih.gov/pubmed/39932770" } @Article{info:doi/10.2196/60616, author="Varghese, Julian and Bickmann, Lucas and Str{\"u}nker, Timo and Neuhaus, Nina and T{\"u}ttelmann, Frank and Sandmann, Sarah", title="Publication Counts in Context: Normalization Using Query and Reference Terms in PubMed", journal="J Med Internet Res", year="2025", month="Feb", day="3", volume="27", pages="e60616", keywords="publication database", keywords="science communication", keywords="citation", keywords="H-index", keywords="normalization", keywords="publication", keywords="trend", keywords="scientometrics", keywords="scholarly", doi="10.2196/60616", url="https://www.jmir.org/2025/1/e60616" } @Article{info:doi/10.2196/63775, author="Li, Rui and Wu, Tong", title="Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis", journal="Interact J Med Res", year="2025", month="Jan", day="30", volume="14", pages="e63775", keywords="artificial intelligence", keywords="medical education", keywords="bibliometric", keywords="citation trends", keywords="academic pattern", keywords="VOSviewer", keywords="Citespace", keywords="AI", abstract="Background: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain. Objective: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century. Methods: Documents were retrieved from the Web of Science Core Collection database from 2000 to 2024. VOSviewer, Incites, and Citespace were used to analyze the bibliometric metrics, which were categorized by country, institution, authors, journals, and keywords. The variables analyzed encompassed counts, citations, H-index, impact factor, and collaboration metrics. Results: Altogether, 7534 publications were initially retrieved and 2775 were included for analysis. The annual count and citation of papers exhibited exponential trends since 2018. The United States emerged as the lead contributor due to its high productivity and recognition levels. Stanford University, Johns Hopkins University, National University of Singapore, Mayo Clinic, University of Arizona, and University of Toronto were representative institutions in their respective fields. Cureus, JMIR Medical Education, Medical Teacher, and BMC Medical Education ranked as the top four most productive journals. The resulting heat map highlighted several high-frequency keywords, including performance, education, AI, and model. The citation burst time of terms revealed that AI technologies shifted from imaging processing (2000), augmented reality (2013), and virtual reality (2016) to decision-making (2020) and model (2021). Keywords such as mortality and robotic surgery persisted into 2023, suggesting the ongoing recognition and interest in these areas. Conclusions: This study provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions. Along with the rapid growth of AI, medical education is expected to gain much more benefits. ", doi="10.2196/63775", url="https://www.i-jmr.org/2025/1/e63775" } @Article{info:doi/10.2196/59598, author="Jiang, Yuyan and Liu, Xue-li and Wang, Liyun", title="Evaluation and Comparison of the Academic Quality of Open-Access Mega Journals and Authoritative Journals: Disruptive Innovation Evaluation", journal="J Med Internet Res", year="2025", month="Jan", day="15", volume="27", pages="e59598", keywords="innovative evaluation", keywords="disruption index", keywords="open-access mega journals", keywords="paper evaluation", keywords="open citation data", abstract="Background: Some scholars who are skeptical about open-access mega journals (OAMJs) have argued that low-quality papers are often difficult to publish in more prestigious and authoritative journals, and OAMJs may be their main destination. Objective: This study aims to evaluate the academic quality of OAMJs and highlight their important role in clinical medicine. To achieve this aim, authoritative journals and representative OAMJs in this field were selected as research objects. The differences between the two were compared and analyzed in terms of their level of disruptive innovation. Additionally, this paper explored the countries and research directions for which OAMJs serve as publication channels for disruptive innovations. Methods: In this study, the journal information, literature data, and open citation relationship data were sourced from Journal Citation Reports (JCR), Web of Science (WoS), InCites, and the OpenCitations Index of PubMed Open PMID-to-PMID citations (POCI). Then, we calculated the disruptive innovation level of the focus paper based on the local POCI database. Results: The mean Journal Disruption Index (JDI) values for the selected authoritative journals and OAMJs were 0.5866 (SD 0.26933) and 0.0255 (SD 0.01689), respectively, showing a significant difference. Only 1.48\% (861/58,181) of the OAMJ papers reached the median level of disruptive innovation of authoritative journal papers (MDAJ). However, the absolute number was roughly equal to that of authoritative journals. OAMJs surpassed authoritative journals in publishing innovative papers in 24 research directions (eg, Allergy), accounting for 40.68\% of all research directions in clinical medicine. Among research topics with at least 10 authoritative papers, OAMJs matched or exceeded MDAJ in 35.71\% of cases. The number of papers published in authoritative journals and the average level of disruptive innovation in each country showed a linear relationship after logarithmic treatment, with a correlation coefficient of --0.891 (P<.001). However, the number of papers published in OAMJs in each country and the average level of disruptive innovation did not show a linear relationship after logarithmic treatment. Conclusions: While the average disruptive innovation level of papers published by OAMJs is significantly lower than that of authoritative journals, OAMJs have become an important publication channel for innovative research in various research directions. They also provide fairer opportunities for the publication of innovative results from limited-income countries. Therefore, the academic community should recognize the contribution and value of OAMJs to advancing scientific research. ", doi="10.2196/59598", url="https://www.jmir.org/2025/1/e59598" } @Article{info:doi/10.2196/65775, author="Sebo, Paul and Sebo, Melissa", title="Geographical Disparities in Research Misconduct: Analyzing Retraction Patterns by Country", journal="J Med Internet Res", year="2025", month="Jan", day="14", volume="27", pages="e65775", keywords="affiliation", keywords="country", keywords="fraud", keywords="integrity", keywords="misconduct", keywords="plagiarism", keywords="publication", keywords="research", keywords="retraction", keywords="ethical standards", keywords="ethics", keywords="research misconduct", keywords="literature", doi="10.2196/65775", url="https://www.jmir.org/2025/1/e65775", url="http://www.ncbi.nlm.nih.gov/pubmed/39808480" } @Article{info:doi/10.2196/58165, author="Han, Qing", title="Topics and Trends of Health Informatics Education Research: Scientometric Analysis", journal="JMIR Med Educ", year="2024", month="Dec", day="11", volume="10", pages="e58165", keywords="health informatics education", keywords="scientometric analysis", keywords="structural topic model", keywords="health informatics", keywords="medical informatics", keywords="medical education", abstract="Background: Academic and educational institutions are making significant contributions toward training health informatics professionals. As research in health informatics education (HIE) continues to grow, it is useful to have a clearer understanding of this research field. Objective: This study aims to comprehensively explore the research topics and trends of HIE from 2014 to 2023. Specifically, it aims to explore (1) the trends of annual articles, (2) the prolific countries/regions, institutions, and publication sources, (3) the scientific collaborations of countries/regions and institutions, and (4) the major research themes and their developmental tendencies. Methods: Using publications in Web of Science Core Collection, a scientometric analysis of 575 articles related to the field of HIE was conducted. The structural topic model was used to identify topics discussed in the literature and to reveal the topic structure and evolutionary trends of HIE research. Results: Research interest in HIE has clearly increased from 2014 to 2023, and is continually expanding. The United States was found to be the most prolific country in this field. Harvard University was found to be the leading institution with the highest publication productivity. Journal of Medical Internet Research, Journal of The American Medical Informatics Association, and Applied Clinical Informatics were the top 3 journals with the highest articles in this field. Countries/regions and institutions having higher levels of international collaboration were more impactful. Research on HIE could be modeled into 7 topics related to the following areas: clinical (130/575, 22.6\%), mobile application (123/575, 21.4\%), consumer (99/575, 17.2\%), teaching (61/575, 10.6\%), public health (56/575, 9.7\%), discipline (55/575, 9.6\%), and nursing (51/575, 8.9\%). The results clearly indicate the unique foci for each year, depicting the process of development for health informatics research. Conclusions: This is believed to be the first scientometric analysis exploring the research topics and trends in HIE. This study provides useful insights and implications, and the findings could be used as a guide for HIE contributors. ", doi="10.2196/58165", url="https://mededu.jmir.org/2024/1/e58165" } @Article{info:doi/10.2196/60057, author="Kaczmarczyk, Robert and Wilhelm, Isabelle Theresa and Roos, Jonas and Martin, Ron", title="Decoding the Digital Pulse: Bibliometric Analysis of 25 Years in Digital Health Research Through the Journal of Medical Internet Research", journal="J Med Internet Res", year="2024", month="Nov", day="15", volume="26", pages="e60057", keywords="digital health", keywords="JMIR publication analysis", keywords="network analysis", keywords="artificial intelligence", keywords="AI", keywords="large language models", keywords="eHealth", keywords="Claude 3 Opus", keywords="digital", keywords="digital technology", keywords="digital intervention", keywords="machine learning", keywords="natural language processing", keywords="NLP", keywords="deep learning", keywords="algorithm", keywords="model", keywords="analytics", keywords="practical model", keywords="pandemic", keywords="postpandemic era", keywords="mobile phone", abstract="Background: As the digital health landscape continues to evolve, analyzing the progress and direction of the field can yield valuable insights. The Journal of Medical Internet Research (JMIR) has been at the forefront of disseminating digital health research since 1999. A comprehensive network analysis of JMIR publications can help illuminate the evolution and trends in digital medicine over the past 25 years. Objective: This study aims to conduct a detailed network analysis of JMIR's publications to uncover the growth patterns, dominant themes, and potential future trajectories in digital health research. Methods: We retrieved 8068 JMIR papers from PubMed using the Biopython library. Keyword metrics were assessed using accuracy, recall, and F1-scores to evaluate the effectiveness of keyword identification from Claude 3 Opus and Gemini 1.5 Pro in addition to 2 conventional natural language processing methods using key bidirectional encoder representations from transformers. Future trends for 2024-2026 were predicted using Claude 3 Opus, Google's Time Series Foundation Model, autoregressive integrated moving average, exponential smoothing, and Prophet. Network visualization techniques were used to represent and analyze the complex relationships between collaborating countries, paper types, and keyword co-occurrence. Results: JMIR's publication volume showed consistent growth, with a peak in 2020. The United States dominated country contributions, with China showing a notable increase in recent years. Keyword analysis from 1999 to 2023 showed significant thematic shifts, from an early internet and digital health focus to the dominance of COVID-19 and advanced technologies such as machine learning. Predictions for 2024-2026 suggest an increased focus on artificial intelligence, digital health, and mental health. Conclusions: Network analysis of JMIR publications provides a macroscopic view of the evolution of the digital health field. The journal's trajectory reflects broader technological advances and shifting research priorities, including the impact of the COVID-19 pandemic. The predicted trends underscore the growing importance of computational technology in future health care research and practice. The findings from JMIR provide a glimpse into the future of digital medicine, suggesting a robust integration of artificial intelligence and continued emphasis on mental health in the postpandemic era. ", doi="10.2196/60057", url="https://www.jmir.org/2024/1/e60057" } @Article{info:doi/10.2196/60025, author="Raman, Raghu and Singhania, Monica and Nedungadi, Prema", title="Advancing the United Nations Sustainable Development Goals Through Digital Health Research: 25 Years of Contributions From the Journal of Medical Internet Research", journal="J Med Internet Res", year="2024", month="Nov", day="4", volume="26", pages="e60025", keywords="sustainable development goal", keywords="topic modeling", keywords="public health", keywords="surveillance", keywords="gender equality", keywords="non-communicable disease", keywords="social media", keywords="COVID-19", keywords="SARS-CoV-2", keywords="coronavirus", keywords="machine learning", keywords="artificial intelligence", keywords="AI", keywords="digital health", doi="10.2196/60025", url="https://www.jmir.org/2024/1/e60025" } @Article{info:doi/10.2196/58987, author="Hu, Jing and Li, Chong and Ge, Yanlei and Yang, Jingyi and Zhu, Siyi and He, Chengqi", title="Mapping the Evolution of Digital Health Research: Bibliometric Overview of Research Hotspots, Trends, and Collaboration of Publications in JMIR (1999-2024)", journal="J Med Internet Res", year="2024", month="Oct", day="17", volume="26", pages="e58987", keywords="JMIR", keywords="bibliometric analysis", keywords="ehealth", keywords="digital health", keywords="medical informatics", keywords="health informatics", keywords="open science", keywords="publishing", abstract="Background: While bibliometric studies of individual journals have been conducted, to the best of our knowledge, bibliometric mapping has not yet been utilized to analyze the literature published by the Journal of Medical Internet Research (JMIR). Objective: In celebration of the journal's 25th anniversary, this study aimed to review the entire collection of JMIR publications from 1999 to 2024 and provide a comprehensive overview of the main publication characteristics. Methods: This study included papers published in JMIR during the 25-year period from 1999 to 2024. The data were analyzed using CiteSpace, VOSviewer, and the ``Bibliometrix'' package in R. Through descriptive bibliometrics, we examined the dynamics and trend patterns of JMIR literature production and identified the most prolific authors, papers, institutions, and countries. Bibliometric maps were used to visualize the content of published articles and to identify the most prominent research terms and topics, along with their evolution. A bibliometric network map was constructed to determine the hot research topics over the past 25 years. Results: This study revealed positive trends in literature production, with both the total number of publications and the average number of citations increasing over the years. And the global COVID-19 pandemic induced an explosive rise in the number of publications in JMIR. The most productive institutions were predominantly from the United States, which ranked highest in successful publications within the journal. The editor-in-chief of JMIR was identified as a pioneer in this field. The thematic analysis indicated that the most prolific topics aligned with the primary aims and scope of the journal. Currently and in the foreseeable future, the main themes of JMIR include ``artificial intelligence,'' ``patient empowerment,'' and ``victimization.'' Conclusions: This bibliometric study highlighted significant contributions to digital health by identifying key research trends, themes, influential authors, and collaborations. The findings underscore the necessity to enhance publications from developing countries, improve gender diversity among authors, and expand the range of research topics explored in the journal. ", doi="10.2196/58987", url="https://www.jmir.org/2024/1/e58987" } @Article{info:doi/10.2196/51411, author="Wang, Shuang and Yang, Liuying and Li, Min and Zhang, Xinghe and Tai, Xiantao", title="Medical Education and Artificial Intelligence: Web of Science--Based Bibliometric Analysis (2013-2022)", journal="JMIR Med Educ", year="2024", month="Oct", day="10", volume="10", pages="e51411", keywords="artificial intelligence", keywords="medical education", keywords="bibliometric analysis", keywords="CiteSpace", keywords="VOSviewer", abstract="Background: Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. Objective: This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013?2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. Methods: Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. Results: A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was ``Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey.'' Keyword analysis revealed that ``radiology,'' ``medical physics,'' ``ehealth,'' ``surgery,'' and ``specialty'' were the primary focus, whereas ``big data'' and ``management'' emerged as research frontiers. Conclusions: The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions. ", doi="10.2196/51411", url="https://mededu.jmir.org/2024/1/e51411" } @Article{info:doi/10.2196/63010, author="Hirosawa, Takanobu and Harada, Yukinori and Tokumasu, Kazuki and Ito, Takahiro and Suzuki, Tomoharu and Shimizu, Taro", title="Comparative Study to Evaluate the Accuracy of Differential Diagnosis Lists Generated by Gemini Advanced, Gemini, and Bard for a Case Report Series Analysis: Cross-Sectional Study", journal="JMIR Med Inform", year="2024", month="Oct", day="2", volume="12", pages="e63010", keywords="artificial intelligence", keywords="clinical decision support", keywords="diagnostic excellence", keywords="generative artificial intelligence", keywords="large language models", keywords="natural language processing", abstract="Background: Generative artificial intelligence (GAI) systems by Google have recently been updated from Bard to Gemini and Gemini Advanced as of December 2023. Gemini is a basic, free-to-use model after a user's login, while Gemini Advanced operates on a more advanced model requiring a fee-based subscription. These systems have the potential to enhance medical diagnostics. However, the impact of these updates on comprehensive diagnostic accuracy remains unknown. Objective: This study aimed to compare the accuracy of the differential diagnosis lists generated by Gemini Advanced, Gemini, and Bard across comprehensive medical fields using case report series. Methods: We identified a case report series with relevant final diagnoses published in the American Journal Case Reports from January 2022 to March 2023. After excluding nondiagnostic cases and patients aged 10 years and younger, we included the remaining case reports. After refining the case parts as case descriptions, we input the same case descriptions into Gemini Advanced, Gemini, and Bard to generate the top 10 differential diagnosis lists. In total, 2 expert physicians independently evaluated whether the final diagnosis was included in the lists and its ranking. Any discrepancies were resolved by another expert physician. Bonferroni correction was applied to adjust the P values for the number of comparisons among 3 GAI systems, setting the corrected significance level at P value <.02. Results: In total, 392 case reports were included. The inclusion rates of the final diagnosis within the top 10 differential diagnosis lists were 73\% (286/392) for Gemini Advanced, 76.5\% (300/392) for Gemini, and 68.6\% (269/392) for Bard. The top diagnoses matched the final diagnoses in 31.6\% (124/392) for Gemini Advanced, 42.6\% (167/392) for Gemini, and 31.4\% (123/392) for Bard. Gemini demonstrated higher diagnostic accuracy than Bard both within the top 10 differential diagnosis lists (P=.02) and as the top diagnosis (P=.001). In addition, Gemini Advanced achieved significantly lower accuracy than Gemini in identifying the most probable diagnosis (P=.002). Conclusions: The results of this study suggest that Gemini outperformed Bard in diagnostic accuracy following the model update. However, Gemini Advanced requires further refinement to optimize its performance for future artificial intelligence--enhanced diagnostics. These findings should be interpreted cautiously and considered primarily for research purposes, as these GAI systems have not been adjusted for medical diagnostics nor approved for clinical use. ", doi="10.2196/63010", url="https://medinform.jmir.org/2024/1/e63010" } @Article{info:doi/10.2196/57772, author="Ba, Hongjun and Zhang, Lili and He, Xiufang and Li, Shujuan", title="Knowledge Mapping and Global Trends in the Field of the Objective Structured Clinical Examination: Bibliometric and Visual Analysis (2004-2023)", journal="JMIR Med Educ", year="2024", month="Sep", day="30", volume="10", pages="e57772", keywords="Objective Structured Clinical Examination", keywords="OSCE", keywords="medical education assessment", keywords="bibliometric analysis", keywords="academic collaboration", keywords="health care professional training", keywords="medical education", keywords="medical knowledge", keywords="medical training", keywords="medical student", abstract="Background: The Objective Structured Clinical Examination (OSCE) is a pivotal tool for assessing health care professionals and plays an integral role in medical education. Objective: This study aims to map the bibliometric landscape of OSCE research, highlighting trends and key influencers. Methods: A comprehensive literature search was conducted for materials related to OSCE from January 2004 to December 2023, using the Web of Science Core Collection database. Bibliometric analysis and visualization were performed with VOSviewer and CiteSpace software tools. Results: Our analysis indicates a consistent increase in OSCE-related publications over the study period, with a notable surge after 2019, culminating in a peak of activity in 2021. The United States emerged as a significant contributor, responsible for 30.86\% (1626/5268) of total publications and amassing 44,051 citations. Coauthorship network analysis highlighted robust collaborations, particularly between the United States and the United Kingdom. Leading journals in this domain---BMC Medical Education, Medical Education, Academic Medicine, and Medical Teacher---featured the highest volume of papers, while The Lancet garnered substantial citations, reflecting its high impact factor (to be verified for accuracy). Prominent authors in the field include Sondra Zabar, Debra Pugh, Timothy J Wood, and Susan Humphrey-Murto, with Ronaldo M Harden, Brian D Hodges, and George E Miller being the most cited. The analysis of key research terms revealed a focus on ``education,'' ``performance,'' ``competence,'' and ``skills,'' indicating these are central themes in OSCE research. Conclusions: The study underscores a dynamic expansion in OSCE research and international collaboration, spotlighting influential countries, institutions, authors, and journals. These elements are instrumental in steering the evolution of medical education assessment practices and suggest a trajectory for future research endeavors. Future work should consider the implications of these findings for medical education and the potential areas for further investigation, particularly in underrepresented regions or emerging competencies in health care training. ", doi="10.2196/57772", url="https://mededu.jmir.org/2024/1/e57772" } @Article{info:doi/10.2196/63367, author="Liu, Lu and Wang, Xiu-Ling and Cheng, Nuo and Yu, Fu-Min and Li, Hui-Jun and Mu, Yang and Yuan, Yonghui and Dong, Jia-Xin and Wu, Yu-Dan and Gong, Da-Xin and Wang, Shuang and Zhang, Guang-Wei", title="Development Trends and Prospects of Technology-Based Solutions for Health Challenges in Aging Over the Past 25 Years: Bibliometric Analysis", journal="J Med Internet Res", year="2024", month="Sep", day="20", volume="26", pages="e63367", keywords="bibliometrics", keywords="CiteSpace", keywords="VOSviewer", keywords="visualization", keywords="aging health", keywords="technological innovations", keywords="tech-based", keywords="technology-based", keywords="technology", keywords="health challenges", keywords="challenges", keywords="trends", keywords="older adults", keywords="older adult", keywords="ageing", keywords="aging", keywords="elder", keywords="elderly", keywords="older person", keywords="older people", keywords="gerontology", keywords="geriatric", keywords="geriatrics", keywords="remote", keywords="remote monitoring", keywords="monitoring", keywords="surveillance", keywords="artificial intelligence", keywords="AI", keywords="AI-driven", keywords="innovation", keywords="innovations", keywords="health management", keywords="telemedicine", keywords="remote care", abstract="Background: As the global population ages, we witness a broad scientific and technological revolution tailored to meet the health challenges of older adults. Over the past 25 years, technological innovations, ranging from advanced medical devices to user-friendly mobile apps, are transforming the way we address these challenges, offering new avenues to enhance the quality of life and well-being of the aging demographic. Objective: This study aimed to systematically review the development trends in technology for managing and caring for the health of older adults over the past 25 years and to project future development prospects. Methods: We conducted a comprehensive bibliometric analysis of literatures related to technology-based solutions for health challenges in aging, published up to March 18, 2024. The search was performed using the Web of Science Core Collection, covering a span from 1999 to 2024. Our search strategy was designed to capture a broad spectrum of terms associated with aging, health challenges specific to older adults, and technological interventions. Results: A total of 1133 publications were found in the Web of Science Core Collection. The publication trend over these 25 years showed a gradual but fluctuating increase. The United States was the most productive country and participated in international collaboration most frequently. The predominant keywords identified through this analysis included ``dementia,'' ``telemedicine,'' ``older-adults,'' ``telehealth,'' and ``care.'' The keywords with citation bursts included ``telemedicine'' and ``digital health.'' Conclusions: The scientific and technological revolution has significantly improved older adult health management, particularly in chronic disease monitoring, mobility, and social connectivity. The momentum for innovation continues to build, with future research likely to focus on predictive analytics and personalized health care solutions, further enhancing older adults' independence and quality of life. ", doi="10.2196/63367", url="https://www.jmir.org/2024/1/e63367", url="http://www.ncbi.nlm.nih.gov/pubmed/39238480" } @Article{info:doi/10.2196/40801, author="Feng, Hanlin and Kurata, Karin and Cao, Jianfei and Itsuki, Kageyama and Niwa, Makoto and Aoyama, Atsushi and Kodama, Kota", title="Telemedicine Research Trends in 2001-2022 and Research Cooperation Between China and Other Countries Before and After the COVID-19 Pandemic: Bibliometric Analysis", journal="Interact J Med Res", year="2024", month="Aug", day="30", volume="13", pages="e40801", keywords="telemedicine", keywords="telehealth", keywords="coauthorship analysis", keywords="network analysis", keywords="bibliometric analysis", keywords="co-occurrence analysis", abstract="Background: Advancements in technology have overcome geographical barriers, making telemedicine, which offers remote emergency services, healthcare, and medication guidance, increasingly popular. COVID-19 restrictions amplified its global importance by bridging distances. Objective: This study aimed to analyze Chinese and global literature data, present new global telemedicine research trends, and clarify the development potential, collaborations, and deficiencies in China's telemedicine research. Methods: We conducted bibliometrics and network analyses on relevant documents from the Web of Science database from 2001 to 2022. Data collection was completed on October 30, 2023. Considering COVID-19's impact, 2020 was used as a baseline, dividing the data into 2 periods: 2001-2019 and 2020-2022. The development potential was determined based on publication trends. An international coauthorship network analysis identified collaboration statuses and potential. Co-occurrence analysis was conducted for China and the world. Results: We identified 25,333 telemedicine-related research papers published between 2001 and 2022, with a substantial increase during the COVID-19 period (2020-2022), particularly in China (1.93-fold increase), moving its global publication rank from tenth to sixth. The United States, the United Kingdom, and Australia contributed 62.96\% of the literature, far ahead of China's 3.90\%. Globally, telemedicine research increased significantly post-2020. Between 2001 and 2019, the United States and Australia were central in coauthor networks; post-2020, the United States remained the largest node. Network hubs included the United States, the United Kingdom, Australia, and Canada. Keyword co-occurrence analysis revealed 5 global clusters from 2001 to 2019 (system technology, health care applications, mobile health, mental health, and electronic health) and 2020 to 2022 (COVID-19, children's mental health, artificial intelligence, digital health, and rehabilitation of middle-aged and older adults). In China, the research trends aligned with global patterns, with rapid growth post-2020. From 2001 to 2019, China cooperated closely with Indonesia, India, Japan, Taiwan, and South Korea. From 2020 to 2022, cooperation expanded to Japan, Singapore, Malaysia, and South Korea, as well as Saudi Arabia, Egypt, South Africa, Ghana, Lebanon, and other African and Middle Eastern countries. Chinese keyword co-occurrence analysis showed focus areas in system technology, health care applications, mobile health, big data analysis, and electronic health (2001-2019) and COVID-19, artificial intelligence, digital health, and mental health (2020-2022). Although psychology research increased, studies on children's mental health and middle-aged and older adults' rehabilitation were limited. Conclusions: We identified the latest trends in telemedicine research, demonstrating its significant potential in China and providing directions for future development and collaborations in telemedicine research. ", doi="10.2196/40801", url="https://www.i-jmr.org/2024/1/e40801" } @Article{info:doi/10.2196/58950, author="Meyer, Annika and Streichert, Thomas", title="Twenty-Five Years of Progress---Lessons Learned From JMIR Publications to Address Gender Parity in Digital Health Authorships: Bibliometric Analysis", journal="J Med Internet Res", year="2024", month="Aug", day="9", volume="26", pages="e58950", keywords="digital health", keywords="medical informatics, authorship", keywords="gender distribution", keywords="diversity", keywords="bibliometric", keywords="scientometric", keywords="algorithmic bias reduction", keywords="gender gap", keywords="JMIR Publications", keywords="authorships", keywords="author", keywords="authors", keywords="bibliometric analysis", keywords="equality", keywords="comparison", keywords="gender representation", keywords="journal", keywords="journals", keywords="article", keywords="articles", keywords="Web of Science", keywords="control group", keywords="comparative analysis", keywords="statistical analysis", keywords="gender", abstract="Background: Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored. Objective: This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship. Methods: This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship. Results: The analysis revealed that 37\% of first authors and 30\% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49\% of first authors and 38\% of last authors being female, yielding odds ratios of 1.96 (95\% CI 1.90-2.03; P<.001) and 1.78 (95\% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field. Conclusions: There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively. ", doi="10.2196/58950", url="https://www.jmir.org/2024/1/e58950" } @Article{info:doi/10.2196/57830, author="Qi, Wenhao and Zhu, Xiaohong and He, Danni and Wang, Bin and Cao, Shihua and Dong, Chaoqun and Li, Yunhua and Chen, Yanfei and Wang, Bingsheng and Shi, Yankai and Jiang, Guowei and Liu, Fang and Boots, M. Lizzy M. and Li, Jiaqi and Lou, Xiajing and Yao, Jiani and Lu, Xiaodong and Kang, Junling", title="Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis", journal="J Med Internet Res", year="2024", month="Aug", day="8", volume="26", pages="e57830", keywords="artificial intelligence", keywords="AI", keywords="biomarker", keywords="dementia", keywords="machine learning", keywords="bibliometric analysis", abstract="Background: With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. Objective: The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. Methods: This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. Results: To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41\% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. Conclusions: The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention. ", doi="10.2196/57830", url="https://www.jmir.org/2024/1/e57830" } @Article{info:doi/10.2196/52020, author="Hu, Zhiyuan and Qin, Xiaoping and Chen, Kaiyan and Huang, Yu-Ni and Wang, Szewei Richard and Tung, Tao-Hsin and Chuang, Yen-Ching and Wang, Bing-Long", title="Chinese Health Insurance in the Digital Era: Bibliometric Study", journal="Interact J Med Res", year="2024", month="Jul", day="23", volume="13", pages="e52020", keywords="telemedicine", keywords="health insurance", keywords="internet plus healthcare", keywords="bibliometric", keywords="VOSviewer", abstract="Background: China has entered the era of digital health care after years of reforms in the health care system. The use of digital technologies in healthcare services is rapidly increasing, indicating the onset of a new period. The reform of health insurance has also entered a new phase. Objective: This study aims to investigate the evolution of health care insurance within the context of telemedicine and Internet Plus Healthcare (IPHC) during the digital health care era by using scientometric methods to analyze publication patterns, influential keywords, and research hot spots. It seeks to understand how health care insurance has adapted to the growing integration of IPHC and telemedicine in health care services and the implications for policy and practice. Methods: A total of 411 high-quality studies were curated from the China National Knowledge Infrastructure (CNKI) database in the Chinese language, scientometric analysis was conducted, and VOSviewer software was used to conduct a visualized analysis of keywords and hot spots in the literature. Results: The number of articles in this field has increased notably from 2000 to 2022 and has increased annually based on a curve of y=0.332exp (0.4002x) with R2=0.6788. In total, 62 institutions and 811 authors have published research articles in the Chinese language in this field. This study included 290 keywords and formulated a total of 5 hot-topic clusters of ``telemedicine,'' ``IPHC,'' ``internet hospital,'' ``health insurance payments,'' and ``health insurance system.'' Conclusions: Studies on the application of digital technologies in health care insurance has evolved from foundational studies to a broader scope. The emergence of internet hospitals has showcased the potential for integrating IPHC services into insurance payment systems. However, this development also highlights the necessity for enhanced interregional coordination mechanisms. The reform of health insurance payment is contingent upon ongoing advancements in digital technology and increased investment in electronic medical records and primary health care services. Future efforts should focus on integrating technology with administrative systems, advancing mobile health care solutions, and ensuring interoperability among various payment systems to improve efficiency and standardize health care services. ", doi="10.2196/52020", url="https://www.i-jmr.org/2024/1/e52020" } @Article{info:doi/10.2196/48259, author="Gu, Wenjun and Wang, Jinhua and Zhang, Yunqi and Liang, Shaolin and Ai, Zisheng and Li, Jiyu", title="Evolution of Digital Health and Exploration of Patented Technologies (2017-2021): Bibliometric Analysis", journal="Interact J Med Res", year="2024", month="Jul", day="11", volume="13", pages="e48259", keywords="technology trends", keywords="digital health", keywords="patent", keywords="bibliometric analysis", keywords="CiteSpace5.1R8", abstract="Background: The significant impact of digital health emerged prominently during the COVID-19 pandemic. Despite this, there is a paucity of bibliometric analyses focusing on technologies within the field of digital health patents. Patents offer a wealth of insights into technologies, commercial prospects, and competitive landscapes, often undisclosed in other publications. Given the rapid evolution of the digital health industry, safeguarding algorithms, software, and advanced surgical devices through patent systems is imperative. The patent system simultaneously acts as a valuable repository of technological knowledge, accessible to researchers. This accessibility facilitates the enhancement of existing technologies and the advancement of medical equipment, ultimately contributing to public health improvement and meeting public demands. Objective: The primary objective of this study is to gain a more profound understanding of technology hotspots and development trends within the field of digital health. Methods: Using a bibliometric analysis methodology, we assessed the global technological output reflected in patents on digital health published between 2017 and 2021. Using Citespace5.1R8 and Excel 2016, we conducted bibliometric visualization and comparative analyses of key metrics, including national contributions, institutional affiliations, inventor profiles, and technology topics. Results: A total of 15,763 digital health patents were identified as published between 2017 and 2021. The China National Intellectual Property Administration secured the top position with 7253 published patents, whereas Koninklijke Philips emerged as the leading institution with 329 patents. Notably, Assaf Govari emerged as the most prolific inventor. Technology hot spots encompassed categories such as ``Medical Equipment and Information Systems,'' ``Image Analysis,'' and ``Electrical Diagnosis,'' classified by Derwent Manual Code. A patent related to the technique of receiving and transmitting data through microchips garnered the highest citation, attributed to the patentee Covidien LP. Conclusions: The trajectory of digital health patents has been growing since 2017, primarily propelled by China, the United States, and Japan. Applications in health interventions and enhancements in surgical devices represent the predominant scenarios for digital health technology. Algorithms emerged as the pivotal technologies protected by patents, whereas techniques related to data transfer, storage, and exchange in the digital health domain are anticipated to be focal points in forthcoming basic research. ", doi="10.2196/48259", url="https://www.i-jmr.org/2024/1/e48259" } @Article{info:doi/10.2196/51347, author="Wang, Meng and Peng, Yun and Wang, Ya and Luo, Dehong", title="Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis", journal="Interact J Med Res", year="2024", month="Jul", day="9", volume="13", pages="e51347", keywords="bibliometric", keywords="radiogenomics", keywords="multiomics", keywords="genomics", keywords="radiomics", abstract="Background: Radiogenomics is an emerging technology that integrates genomics and medical image--based radiomics, which is considered a promising approach toward achieving precision medicine. Objective: The aim of this study was to quantitatively analyze the research status, dynamic trends, and evolutionary trajectory in the radiogenomics field using bibliometric methods. Methods: The relevant literature published up to 2023 was retrieved from the Web of Science Core Collection. Excel was used to analyze the annual publication trend. VOSviewer was used for constructing the keywords co-occurrence network and the collaboration networks among countries and institutions. CiteSpace was used for citation keywords burst analysis and visualizing the references timeline. Results: A total of 3237 papers were included and exported in plain-text format. The annual number of publications showed an increasing annual trend. China and the United States have published the most papers in this field, with the highest number of citations in the United States and the highest average number per item in the Netherlands. Keywords burst analysis revealed that several keywords, including ``big data,'' ``magnetic resonance spectroscopy,'' ``renal cell carcinoma,'' ``stage,'' and ``temozolomide,'' experienced a citation burst in recent years. The timeline views demonstrated that the references can be categorized into 8 clusters: lower-grade glioma, lung cancer histology, lung adenocarcinoma, breast cancer, radiation-induced lung injury, epidermal growth factor receptor mutation, late radiotherapy toxicity, and artificial intelligence. Conclusions: The field of radiogenomics is attracting increasing attention from researchers worldwide, with the United States and the Netherlands being the most influential countries. Exploration of artificial intelligence methods based on big data to predict the response of tumors to various treatment methods represents a hot spot research topic in this field at present. ", doi="10.2196/51347", url="https://www.i-jmr.org/2024/1/e51347", url="http://www.ncbi.nlm.nih.gov/pubmed/38980713" } @Article{info:doi/10.2196/50698, author="Stupnicki, Aleksander and Suresh, Basil and Jain, Saurabh", title="Online Visibility and Scientific Relevance of Strabismus Research: Bibliometric Analysis", journal="Interact J Med Res", year="2024", month="Jun", day="12", volume="13", pages="e50698", keywords="strabismus research", keywords="squint", keywords="social media", keywords="scientific relevance", keywords="altmetrics", keywords="accuracy", keywords="medical knowledge", keywords="metric", keywords="bibliometric analysis", keywords="research", keywords="strabismus", keywords="online visibility", keywords="platform", keywords="evidence-based information", keywords="accessibility", abstract="Background: Quality and accuracy of online scientific data are crucial, given that the internet and social media serve nowadays as primary sources of medical knowledge. Objective: This study aims to analyze the relationship between scientific relevance and online visibility of strabismus research to answer the following questions: (1) Are the most popular strabismus papers scientifically relevant? (2) Are the most high-impact strabismus studies shared enough online? Methods: The Altmetric Attention Score (AAS) was used as a proxy for online visibility, whereas citations and the journal's impact factor (IF) served as a metric for scientific relevance. Using ``strabismus'' as a keyword, 100 papers with the highest AAS and 100 papers with the highest number of citations were identified. Statistical analyses, including the Spearman rank test, linear regression, and factor analysis, were performed to assess the relationship between AAS, citations, a journal's IF, and mentions across 18 individual Web 2.0 platforms. Results: A weak, positive, statistically significant correlation was observed between normalized AAS and normalized citations (P<.001; r=0.27) for papers with high visibility. Only Twitter mentions and Mendeley readers correlated significantly with normalized citations (P=.02 and P<.001, respectively) and IF (P=.04 and P=.009, respectively), with Twitter being the strongest significant predictor of citation numbers (r=0.53). For high-impact papers, no correlation was found between normalized citations and normalized AAS (P=.12) or the IF of the journal (P=.55). Conclusions: While clinical relevance influences online attention, most high-impact research related to strabismus is not sufficiently shared on the web. Therefore, researchers should make a greater effort to share high-impact papers related to strabismus on online media platforms to improve accessibility and quality of evidence-based knowledge for patients. ", doi="10.2196/50698", url="https://www.i-jmr.org/2024/1/e50698", url="http://www.ncbi.nlm.nih.gov/pubmed/38865170" } @Article{info:doi/10.2196/52461, author="He, Yuanhang and Xie, Zhihong and Li, Jiachen and Meng, Ziang and Xue, Dongbo and Hao, Chenjun", title="Global Trends in mHealth and Medical Education Research: Bibliometrics and Knowledge Graph Analysis", journal="JMIR Med Educ", year="2024", month="Jun", day="4", volume="10", pages="e52461", keywords="mHealth", keywords="mobile health", keywords="medical education", keywords="bibliometric", keywords="knowledge map", keywords="VOSviewer", abstract="Background: Mobile health (mHealth) is an emerging mobile communication and networking technology for health care systems. The integration of mHealth in medical education is growing extremely rapidly, bringing new changes to the field. However, no study has analyzed the publication and research trends occurring in both mHealth and medical education. Objective: The aim of this study was to summarize the current application and development trends of mHealth in medical education by searching and analyzing published articles related to both mHealth and medical education. Methods: The literature related to mHealth and medical education published from 2003 to 2023 was searched in the Web of Science core database, and 790 articles were screened according to the search strategy. The HistCite Pro 2.0 tool was used to analyze bibliometric indicators. VOSviewer, Pajek64, and SCImago Graphica software were used to visualize research trends and identify hot spots in the field. Results: In the past two decades, the number of published papers on mHealth in medical education has gradually increased, from only 3 papers in 2003 to 130 in 2022; this increase became particularly evident in 2007. The global citation score was determined to be 10,600, with an average of 13.42 citations per article. The local citation score was 96. The United States is the country with the most widespread application of mHealth in medical education, and most of the institutions conducting in-depth research in this field are also located in the United States, closely followed by China and the United Kingdom. Based on current trends, global coauthorship and research exchange will likely continue to expand. Among the research journals publishing in this joint field, journals published by JMIR Publications have an absolute advantage. A total of 105 keywords were identified, which were divided into five categories pointing to different research directions. Conclusions: Under the influence of COVID-19, along with the popularization of smartphones and modern communication technology, the field of combining mHealth and medical education has become a more popular research direction. The concept and application of digital health will be promoted in future developments of medical education. ", doi="10.2196/52461", url="https://mededu.jmir.org/2024/1/e52461" } @Article{info:doi/10.2196/55121, author="Jiang, Yuyan and Liu, Xue-li and Zhang, Zixuan and Yang, Xinru", title="Evaluation and Comparison of Academic Impact and Disruptive Innovation Level of Medical Journals: Bibliometric Analysis and Disruptive Evaluation", journal="J Med Internet Res", year="2024", month="May", day="31", volume="26", pages="e55121", keywords="medical journals", keywords="journal evaluation", keywords="innovative evaluation", keywords="journal disruption index", keywords="disruptive innovation", keywords="academic impact", keywords="peer review", abstract="Background: As an important platform for researchers to present their academic findings, medical journals have a close relationship between their evaluation orientation and the value orientation of their published research results. However, the differences between the academic impact and level of disruptive innovation of medical journals have not been examined by any study yet. Objective: This study aims to compare the relationships and differences between the academic impact, disruptive innovation levels, and peer review results of medical journals and published research papers. We also analyzed the similarities and differences in the impact evaluations, disruptive innovations, and peer reviews for different types of medical research papers and the underlying reasons. Methods: The general and internal medicine Science Citation Index Expanded (SCIE) journals in 2018 were chosen as the study object to explore the differences in the academic impact and level of disruptive innovation of medical journals based on the OpenCitations Index of PubMed open PMID-to-PMID citations (POCI) and H1Connect databases, respectively, and we compared them with the results of peer review. Results: First, the correlation coefficients of the Journal Disruption Index (JDI) with the Journal Cumulative Citation for 5 years (JCC5), Journal Impact Factor (JIF), and Journal Citation Indicator (JCI) were 0.677, 0.585, and 0.621, respectively. The correlation coefficient of the absolute disruption index (Dz) with the Cumulative Citation for 5 years (CC5) was 0.635. However, the average difference in the disruptive innovation and academic influence rankings of journals reached 20 places (about 17.5\%). The average difference in the disruptive innovation and influence rankings of research papers reached about 2700 places (about 17.7\%). The differences reflect the essential difference between the two evaluation systems. Second, the top 7 journals selected based on JDI, JCC5, JIF, and JCI were the same, and all of them were H-journals. Although 8 (8/15, 53\%), 96 (96/150, 64\%), and 880 (880/1500, 58.67\%) of the top 0.1\%, top 1\%, and top 10\% papers selected based on Dz and CC5, respectively, were the same. Third, research papers with the ``changes clinical practice'' tag showed only moderate innovation (4.96) and impact (241.67) levels but had high levels of peer-reviewed recognition (6.00) and attention (2.83). Conclusions: The results of the study show that research evaluation based on innovative indicators is detached from the traditional impact evaluation system. The 3 evaluation systems (impact evaluation, disruptive innovation evaluation, and peer review) only have high consistency for authoritative journals and top papers. Neither a single impact indicator nor an innovative indicator can directly reflect the impact of medical research for clinical practice. How to establish an integrated, comprehensive, scientific, and reasonable journal evaluation system to improve the existing evaluation system of medical journals still needs further research. ", doi="10.2196/55121", url="https://www.jmir.org/2024/1/e55121", url="http://www.ncbi.nlm.nih.gov/pubmed/38820583" } @Article{info:doi/10.2196/54490, author="Wu, MeiJung and Islam, Mohaimenul Md and Poly, Nasrin Tahmina and Lin, Ming-Chin", title="Application of AI in Sepsis: Citation Network Analysis and Evidence Synthesis", journal="Interact J Med Res", year="2024", month="Apr", day="15", volume="13", pages="e54490", keywords="AI", keywords="artificial intelligence", keywords="bibliometric analysis", keywords="bibliometric", keywords="citation", keywords="deep learning", keywords="machine learning", keywords="network analysis", keywords="publication", keywords="sepsis", keywords="trend", keywords="visualization", keywords="VOSviewer", keywords="Web of Science", keywords="WoS", abstract="Background: Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions. Objective: The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis. Methods: We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords. Results: A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6\%), Frontiers in Medicine (17/259, 6.6\%), and Scientific Reports (11/259, 4.2\%). The United States (103/259, 39.8\%), China (83/259, 32\%), United Kingdom (14/259, 5.4\%), and Taiwan (12/259, 4.6\%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023). Conclusions: This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis. ", doi="10.2196/54490", url="https://www.i-jmr.org/2024/1/e54490", url="http://www.ncbi.nlm.nih.gov/pubmed/38621231" } @Article{info:doi/10.2196/52935, author="Mugaanyi, Joseph and Cai, Liuying and Cheng, Sumei and Lu, Caide and Huang, Jing", title="Evaluation of Large Language Model Performance and Reliability for Citations and References in Scholarly Writing: Cross-Disciplinary Study", journal="J Med Internet Res", year="2024", month="Apr", day="5", volume="26", pages="e52935", keywords="large language models", keywords="accuracy", keywords="academic writing", keywords="AI", keywords="cross-disciplinary evaluation", keywords="scholarly writing", keywords="ChatGPT", keywords="GPT-3.5", keywords="writing tool", keywords="scholarly", keywords="academic discourse", keywords="LLMs", keywords="machine learning algorithms", keywords="NLP", keywords="natural language processing", keywords="citations", keywords="references", keywords="natural science", keywords="humanities", keywords="chatbot", keywords="artificial intelligence", abstract="Background: Large language models (LLMs) have gained prominence since the release of ChatGPT in late 2022. Objective: The aim of this study was to assess the accuracy of citations and references generated by ChatGPT (GPT-3.5) in two distinct academic domains: the natural sciences and humanities. Methods: Two researchers independently prompted ChatGPT to write an introduction section for a manuscript and include citations; they then evaluated the accuracy of the citations and Digital Object Identifiers (DOIs). Results were compared between the two disciplines. Results: Ten topics were included, including 5 in the natural sciences and 5 in the humanities. A total of 102 citations were generated, with 55 in the natural sciences and 47 in the humanities. Among these, 40 citations (72.7\%) in the natural sciences and 36 citations (76.6\%) in the humanities were confirmed to exist (P=.42). There were significant disparities found in DOI presence in the natural sciences (39/55, 70.9\%) and the humanities (18/47, 38.3\%), along with significant differences in accuracy between the two disciplines (18/55, 32.7\% vs 4/47, 8.5\%). DOI hallucination was more prevalent in the humanities (42/55, 89.4\%). The Levenshtein distance was significantly higher in the humanities than in the natural sciences, reflecting the lower DOI accuracy. Conclusions: ChatGPT's performance in generating citations and references varies across disciplines. Differences in DOI standards and disciplinary nuances contribute to performance variations. Researchers should consider the strengths and limitations of artificial intelligence writing tools with respect to citation accuracy. The use of domain-specific models may enhance accuracy. ", doi="10.2196/52935", url="https://www.jmir.org/2024/1/e52935", url="http://www.ncbi.nlm.nih.gov/pubmed/38578685" } @Article{info:doi/10.2196/52462, author="Raja, Hina and Munawar, Asim and Mylonas, Nikolaos and Delsoz, Mohammad and Madadi, Yeganeh and Elahi, Muhammad and Hassan, Amr and Abu Serhan, Hashem and Inam, Onur and Hernandez, Luis and Chen, Hao and Tran, Sang and Munir, Wuqaas and Abd-Alrazaq, Alaa and Yousefi, Siamak", title="Automated Category and Trend Analysis of Scientific Articles on Ophthalmology Using Large Language Models: Development and Usability Study", journal="JMIR Form Res", year="2024", month="Mar", day="22", volume="8", pages="e52462", keywords="Bidirectional and Auto-Regressive Transformers", keywords="BART", keywords="bidirectional encoder representations from transformers", keywords="BERT", keywords="ophthalmology", keywords="text classification", keywords="large language model", keywords="LLM", keywords="trend analysis", abstract="Background: In this paper, we present an automated method for article classification, leveraging the power of large language models (LLMs). Objective: The aim of this study is to evaluate the applicability of various LLMs based on textual content of scientific ophthalmology papers. Methods: We developed a model based on natural language processing techniques, including advanced LLMs, to process and analyze the textual content of scientific papers. Specifically, we used zero-shot learning LLMs and compared Bidirectional and Auto-Regressive Transformers (BART) and its variants with Bidirectional Encoder Representations from Transformers (BERT) and its variants, such as distilBERT, SciBERT, PubmedBERT, and BioBERT. To evaluate the LLMs, we compiled a data set (retinal diseases [RenD] ) of 1000 ocular disease--related articles, which were expertly annotated by a panel of 6 specialists into 19 distinct categories. In addition to the classification of articles, we also performed analysis on different classified groups to find the patterns and trends in the field. Results: The classification results demonstrate the effectiveness of LLMs in categorizing a large number of ophthalmology papers without human intervention. The model achieved a mean accuracy of 0.86 and a mean F1-score of 0.85 based on the RenD data set. Conclusions: The proposed framework achieves notable improvements in both accuracy and efficiency. Its application in the domain of ophthalmology showcases its potential for knowledge organization and retrieval. We performed a trend analysis that enables researchers and clinicians to easily categorize and retrieve relevant papers, saving time and effort in literature review and information gathering as well as identification of emerging scientific trends within different disciplines. Moreover, the extendibility of the model to other scientific fields broadens its impact in facilitating research and trend analysis across diverse disciplines. ", doi="10.2196/52462", url="https://formative.jmir.org/2024/1/e52462", url="http://www.ncbi.nlm.nih.gov/pubmed/38517457" } @Article{info:doi/10.2196/49905, author="Hashiguchi, Akiko and Asashima, Makoto and Takahashi, Satoru", title="The Influence of Human Connections and Collaboration on Research Grant Success at Various Career Stages: Regression Analysis", journal="JMIR Form Res", year="2024", month="Feb", day="28", volume="8", pages="e49905", keywords="biomedical researchers", keywords="grant success", keywords="human connection", keywords="peer researchers", keywords="synergistic collaborations", keywords="research development", abstract="Background: Documenting the grant acquisition characteristics of a highly selective group of researchers could provide insights into the research and faculty development of talented individuals, and the insights gained to foster such researchers will help university management strengthen their research capacity. Objective: This study examines the role of human connections in the success of biomedical researchers in Japanese universities. Methods: This study used grant data from the Grants-in-Aid for Scientific Research (GIA) program, the largest competitive research funding program in Japan, to collect information on projects and their implementation systems obtained throughout the participants' careers. Grant success was measured by the number and amounts of the awards obtained while participants occupied the role of principal investigator. Human connections were quantified by the number of projects in which the participants took part as members and were classified by their relationship with the project leader. Data were matched with information on career history, publication performance, and experience of the participants with government-funded programs apart from GIA and were analyzed using univariate and multivariate regression analyses. Results: Early-career interpersonal relationships, as measured using the h-index value of the researchers who provided the participants with their initial experience as project members, had a positive effect on grant success. The experience of contributing to prestigious research programs led by top researchers dramatically increased the cumulative amount of GIA awards received by the participants over time. Univariate logistic regression analyses revealed that more interactions with upper-level researchers resulted in fewer acquisitions of large programs (odds ratio [OR] 0.67, 95\% CI 0.50-0.89). Collaboration with peers increased the success rate of ?2 research grants in large programs in situations in which both the participant and project leader were professors (OR 1.16, 95\% CI 1.06-1.26). Tracking the process of research development, we found that collaboration during the periods of 10 to 14 years and 15 to 19 years after completing a doctorate degree determined the size of the project that the participant would obtain---interactions with peer researchers and subordinates during the 10- to 14-year postdegree period had positive effects on ?2 large-program acquisitions (OR 1.51, 95\% CI 1.09-2.09 and OR 1.31, 95\% CI 1.10-1.57, respectively), whereas interactions with subordinates during the 15- to 19-year postdegree period also had positive effects (OR 1.25, 95\% CI 1.06-1.47). Furthermore, relationships that remained narrowly focused resulted in limited grant success for small programs. Conclusions: Human networking is important for improving an individual's ability to obtain external funding. The results emphasize the importance of having a high-h-indexed collaborator to obtain quality information early in one's career; working with diverse, nonsupervisory personnel at the midcareer stage; and engaging in synergistic collaborations upon establishing a research area in which one can take more initiatives. ", doi="10.2196/49905", url="https://formative.jmir.org/2024/1/e49905", url="http://www.ncbi.nlm.nih.gov/pubmed/38416548" } @Article{info:doi/10.2196/54349, author="Ni, Zhao and Peng, L. Mary and Balakrishnan, Vimala and Tee, Vincent and Azwa, Iskandar and Saifi, Rumana and Nelson, E. LaRon and Vlahov, David and Altice, L. Frederick", title="Implementation of Chatbot Technology in Health Care: Protocol for a Bibliometric Analysis", journal="JMIR Res Protoc", year="2024", month="Feb", day="15", volume="13", pages="e54349", keywords="artificial intelligence", keywords="AI", keywords="bibliometric analysis", keywords="chatbots", keywords="health care", keywords="health promotion", abstract="Background: Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings. Objective: This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health. Methods: In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as ``chatbot,'' ``virtual agent,'' ``virtual assistant,'' ``conversational agent,'' ``conversational AI,'' ``interactive agent,'' ``health,'' and ``healthcare.'' Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks. Results: The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024. Conclusions: Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users. International Registered Report Identifier (IRRID): PRR1-10.2196/54349 ", doi="10.2196/54349", url="https://www.researchprotocols.org/2024/1/e54349", url="http://www.ncbi.nlm.nih.gov/pubmed/38228575" } @Article{info:doi/10.2196/45815, author="Shi, Jin and Bendig, David and Vollmar, Christian Horst and Rasche, Peter", title="Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study", journal="J Med Internet Res", year="2023", month="Dec", day="8", volume="25", pages="e45815", keywords="artificial intelligence", keywords="AI", keywords="AI in medicine", keywords="medical AI taxonomy", keywords="Python", keywords="latent Dirichlet allocation", keywords="LDA", keywords="topic modeling", keywords="unsupervised machine learning", abstract="Background: Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. Objective: In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Methods: Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. Results: From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. Conclusions: The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus. ", doi="10.2196/45815", url="https://www.jmir.org/2023/1/e45815", url="http://www.ncbi.nlm.nih.gov/pubmed/38064255" } @Article{info:doi/10.2196/48529, author="Sebo, Paul and Schwarz, Jo{\"e}lle and Achtari, Margaux and Clair, Carole", title="Women Are Underrepresented Among Authors of Retracted Publications: Retrospective Study of 134 Medical Journals", journal="J Med Internet Res", year="2023", month="Oct", day="6", volume="25", pages="e48529", keywords="error", keywords="gender", keywords="misconduct", keywords="publication", keywords="research", keywords="retraction", keywords="scientific integrity", keywords="woman", keywords="women", keywords="publish", keywords="publishing", keywords="inequality", keywords="retractions", keywords="integrity", keywords="fraud", keywords="plagiarism", keywords="research study", keywords="research article", keywords="scientific research", keywords="journal", keywords="retrospective", doi="10.2196/48529", url="https://www.jmir.org/2023/1/e48529", url="http://www.ncbi.nlm.nih.gov/pubmed/37801343" } @Article{info:doi/10.2196/49721, author="Karystianis, George and Simpson, Paul and Lukmanjaya, Wilson and Ginnivan, Natasha and Nenadic, Goran and Buchan, Iain and Butler, Tony", title="Automatic Extraction of Research Themes in Epidemiological Criminology From PubMed Abstracts From 1946 to 2020: Text Mining Study", journal="JMIR Form Res", year="2023", month="Sep", day="22", volume="7", pages="e49721", keywords="epidemiology", keywords="study determinant", keywords="study outcome", keywords="PubMed", keywords="research priorities", keywords="epidemiological criminology", keywords="criminology", keywords="open research", abstract="Background: The emerging field of epidemiological criminology studies the intersection between public health and justice systems. To increase the value of and reduce waste in research activities in this area, it is important to perform transparent research priority setting considering the needs of research beneficiaries and end users along with a systematic assessment of the existing research activities to address gaps and harness opportunities. Objective: In this study, we aimed to examine published research outputs in epidemiological criminology to assess gaps between published outputs and current research priorities identified by prison stakeholders. Methods: A rule-based method was applied to 23,904 PubMed epidemiological criminology abstracts to extract the study determinants and outcomes (ie, ``themes''). These were mapped against the research priorities identified by Australian prison stakeholders to assess the differences from research outputs. The income level of the affiliation country of the first authors was also identified to compare the ranking of research priorities in countries categorized by income levels. Results: On an evaluation set of 100 abstracts, the identification of themes returned an F1-score of 90\%, indicating reliable performance. More than 53.3\% (11,927/22,361) of the articles had at least 1 extracted theme; the most common was substance use (1533/11,814, 12.97\%), followed by HIV (1493/11,814, 12.64\%). The infectious disease category (2949/11,814, 24.96\%) was the most common research priority category, followed by mental health (2840/11,814, 24.04\%) and alcohol and other drug use (2433/11,814, 20.59\%). A comparison between the extracted themes and the stakeholder priorities showed an alignment for mental health, infectious diseases, and alcohol and other drug use. Although behavior- and juvenile-related themes were common, they did not feature as prison priorities. Most studies were conducted in high-income countries (10,083/11,814, 85.35\%), while countries with the lowest income status focused half of their research on infectious diseases (47/91, 52\%). Conclusions: The identification of research themes from PubMed epidemiological criminology research abstracts is possible through the application of a rule-based text mining method. The frequency of the investigated themes may reflect historical developments concerning disease prevalence, treatment advances, and the social understanding of illness and incarcerated populations. The differences between income status groups are likely to be explained by local health priorities and immediate health risks. Notable gaps between stakeholder research priorities and research outputs concerned themes that were more focused on social factors and systems and may reflect publication bias or self-publication selection, highlighting the need for further research on prison health services and the social determinants of health. Different jurisdictions, countries, and regions should undertake similar systematic and transparent research priority--setting processes. ", doi="10.2196/49721", url="https://formative.jmir.org/2023/1/e49721", url="http://www.ncbi.nlm.nih.gov/pubmed/37738080" } @Article{info:doi/10.2196/45322, author="Ide, Kazuki", title="The Skewed Impact of Highly Cited Articles on Journal Impact Factor", journal="J Med Internet Res", year="2023", month="Sep", day="18", volume="25", pages="e45322", keywords="COVID-19", keywords="journal impact factor", keywords="JIF", keywords="scientometrics", keywords="bibliometrics", keywords="infometrics", keywords="journal", keywords="assessment", keywords="research", keywords="resources", keywords="medical journal", keywords="literature", keywords="database", keywords="community", keywords="behavior", doi="10.2196/45322", url="https://www.jmir.org/2023/1/e45322", url="http://www.ncbi.nlm.nih.gov/pubmed/37721788" } @Article{info:doi/10.2196/47553, author="Hu, Yuanjia and Lu, Yang and Tian, Chenghua and He, Yunfan and Rong, Kaiyi and Pan, Sijia and Lei, Jianbo", title="Current Status and Trends in mHealth-Based Research for Treatment and Intervention in Tinnitus: Bibliometric and Comparative Product Analysis", journal="JMIR Mhealth Uhealth", year="2023", month="Aug", day="24", volume="11", pages="e47553", keywords="tinnitus", keywords="mobile health", keywords="mHealth", keywords="internet", keywords="application", keywords="software", keywords="bibliometrics", keywords="mobile phone", abstract="Background: As a global medical problem, tinnitus can seriously harm human health and is difficult to alleviate, ranking among the top 3 complex diseases in the otolaryngology field. Traditional cognitive behavioral therapy and sound therapy require offline face-to-face treatment with medical staff and have limited effectiveness. Mobile health (mHealth), which, in recent decades, has been greatly applied in the field of rehabilitation health care, improving access to health care resources and the quality of services, has potential research value in the adjunctive treatment of tinnitus. Objective: This study aimed to understand the research trends, product characteristics, problems, and research transformation of tinnitus treatment software by analyzing the research progress of mHealth for tinnitus treatment based on the literature and related marketed apps. Methods: Bibliometric methods were used to describe the characteristics of the relevant literature in terms of the number and topics of publications, authors, and institutions. We further compared the features and limitations of the currently available tinnitus treatment software. Results: Data published until February 28, 2022, were collected. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standardized screening process, 75 papers were included. The country with the highest number of publications was Germany, followed by the United Kingdom and the United States, whereas China had only a single relevant study. The most frequently found journals were the American Journal of Audiology and the Journal of the American Academy of Audiology (18/75, 24\%). With regard to publication topics, cognitive behavioral therapy started to become a hot topic in 2017, and research on mHealth apps has increased. In this study, 28 tinnitus treatment apps were obtained (n=24, 86\% from product data and n=4, 14\% from literature data); these apps were developed mainly in the United States (10/28, 36\%) or China (9/28, 32\%). The main treatment methods were sound therapy (10/28, 36\%) and cognitive behavioral therapy (2/28, 7\%). Of the 75 publications, 7 (9\%) described apps in the market stage. Of the 28 apps, 22 (79\%) lacked literature studies or evidence from professional bodies. Conclusions: We found that, as a whole, the use of mHealth for treatment and intervention in tinnitus was showing a rapid development, in which good progress had been made in studies around sound therapy and cognitive behavioral therapy, although most of the studies (50/75, 67\%) focused on treatment effects. However, the field is poorly accepted in top medical journals, and the majority are in the research design phase, with a lack of translation of the literature results and clinical validation of the marketed apps. Furthermore, in the future, novel artificial intelligence techniques should be used to address the issue of staged monitoring of tinnitus. ", doi="10.2196/47553", url="https://mhealth.jmir.org/2023/1/e47553", url="http://www.ncbi.nlm.nih.gov/pubmed/37616044" } @Article{info:doi/10.2196/46620, author="Zhu, Harrison and Narayana, Vishnu and Zhou, Kelvin and Patel, B. Anisha", title="Altmetric Analysis of Dermatology Manuscript Dissemination During the COVID-19 Era: Cross-Sectional Study", journal="JMIR Dermatol", year="2023", month="Aug", day="16", volume="6", pages="e46620", keywords="altmetric", keywords="media dissemination", keywords="citation number", keywords="bibliometric", keywords="attention score", keywords="social media", abstract="Background: Alternative bibliometrics or altmetrics, is a measure of an academic article's impact on social media outlets, which is quantified by the Altmetric Attention score (AAS). Given a lack of data for altmetric trends during the COVID-19 pandemic, we conducted a comprehensive, multivariable analysis of top dermatology manuscripts published during this time period. Objective: We aim to assess (1) the relationship between traditional bibiliometrics and Altmetrics and (2) factors associated with high AAS. Methods: All abstracted articles published in the top-5 (ranked by SCImago Journal Rankings) peer-reviewed dermatology journals published in 2021 were included in our study. We collected AAS as the dependent variable and categorical predictor variables included journal title, whether a conflict of interest existed, open access status, whether the article was related to COVID-19 or skin-of-color research, and the type of research (eg, clinical, basic science, review, etc). Numerical predictor variables consisted of the impact factor of journal, total citations, and number of authors. Multivariable linear or logistic regression models were used. Results: The relationship between AAS and citation number was significant by multivariable analysis during the COVID-19 pandemic (P<.001). Numerous factors, including studies related to COVID-19, whether the article was open access, title of the journal, and journal impact factor were also independently related to higher AAS (P<.002). Conclusions: Our results validate the use of altmetrics as a complement to traditional bibliometrics, especially in times of widespread scientific interest. Despite existing in a complex realm of bibliometrics, there are also discernable patterns associated with higher AAS. This is especially relevant in the era of growing technologic importance and utility to assess the impact of scientific works within the general public. ", doi="10.2196/46620", url="https://derma.jmir.org/2023/1/e46620", url="http://www.ncbi.nlm.nih.gov/pubmed/37585241" } @Article{info:doi/10.2196/46042, author="Wang, Na and Zhang, Runxi and Ye, Zeyan and Lan, Guanghua and Zhu, Qiuying and Chen, Huanhuan and Zhang, Xiangjun and Tan, Shengkui and Ruan, Yuhua and Lin, Mei", title="Studies on HIV/AIDS Among Students: Bibliometric Analysis", journal="Interact J Med Res", year="2023", month="Aug", day="4", volume="12", pages="e46042", keywords="bibliometric analysis", keywords="HIV", keywords="acquired immunodeficiency syndrome", keywords="AIDS", keywords="student", keywords="university", keywords="college", keywords="postsecondary", keywords="bibliometric", keywords="communicable", keywords="sexually transmitted disease", keywords="STD", keywords="sexual transmission", keywords="sexually transmitted infection", keywords="STI", abstract="Background: In recent years, HIV infection in students has been an ongoing concern worldwide. A large number of articles have been published; however, statistical analysis of the data presented in these publications is lacking. Objective: This study aimed to detect and analyze emerging trends and collaborative networks in research on HIV/AIDS among students. Methods: Research publications on HIV/AIDS among students from 1985 to 2022 were collected from the Web of Science Core Collection. A topic search was used for this study, and articles in English were included. CiteSpace was used to generate visual networks of countries/regions, institutions, references, and keywords. Citation analysis was used to discover milestones in the field and trace the roots of the knowledge base. Keyword analysis was used to detect research hotspots and predict future trends. Results: A total of 2726 publications met the inclusion criteria. Over the past 38 years, the number of publications annually has been on the rise overall. The United States had the highest number of publications (n=1303) and the highest centrality (0.91). The University of California system was the core institution. The main target population of studies on HIV/AIDS among students were medical and university students. These studies focused on students' knowledge, attitudes, risk behaviors, and education about HIV/AIDS. The recent bursting keywords (gay, sexual health, adherence, barriers, mental health, HIV testing, stigma, and antiretroviral therapy) revealed research trends and public interest on this topic. Conclusions: This study identified countries/regions and institutions contributing to the research area of HIV/AIDS among students and revealed research hotspots and emerging trends. The field of research on HIV/AIDS among students was growing rapidly. The United States was at the center, and the University of California system was the core institution. However, academic collaboration should be strengthened. Future research may focus on exploring gay students, sexual health, adherence, barriers, mental health, HIV testing, stigma, and antiretroviral therapy. ", doi="10.2196/46042", url="https://www.i-jmr.org/2023/1/e46042", url="http://www.ncbi.nlm.nih.gov/pubmed/37540553" } @Article{info:doi/10.2196/45059, author="Agnello, Marie Danielle and Loisel, Armand Quentin Emile and An, Qingfan and Balaskas, George and Chrifou, Rabab and Dall, Philippa and de Boer, Janneke and Delfmann, Rahel Lea and Gin{\'e}-Garriga, Maria and Goh, Kunshan and Longworth, Raffaella Giuliana and Messiha, Katrina and McCaffrey, Lauren and Smith, Niamh and Steiner, Artur and Vogelsang, Mira and Chastin, Sebastien", title="Establishing a Health CASCADE--Curated Open-Access Database to Consolidate Knowledge About Co-Creation: Novel Artificial Intelligence--Assisted Methodology Based on Systematic Reviews", journal="J Med Internet Res", year="2023", month="Jul", day="18", volume="25", pages="e45059", keywords="co-creation", keywords="co-production", keywords="co-design", keywords="database", keywords="participatory", keywords="methodology", keywords="artificial intelligence", abstract="Background: Co-creation is an approach that aims to democratize research and bridge the gap between research and practice, but the potential fragmentation of knowledge about co-creation has hindered progress. A comprehensive database of published literature from multidisciplinary sources can address this fragmentation through the integration of diverse perspectives, identification and dissemination of best practices, and increase clarity about co-creation. However, two considerable challenges exist. First, there is uncertainty about co-creation terminology, making it difficult to identify relevant literature. Second, the exponential growth of scientific publications has led to an overwhelming amount of literature that surpasses the human capacity for a comprehensive review. These challenges hinder progress in co-creation research and underscore the need for a novel methodology to consolidate and investigate the literature. Objective: This study aimed to synthesize knowledge about co-creation across various fields through the development and application of an artificial intelligence (AI)--assisted selection process. The ultimate goal of this database was to provide stakeholders interested in co-creation with relevant literature. Methods: We created a novel methodology for establishing a curated database. To accommodate the variation in terminology, we used a broad definition of co-creation that encompassed the essence of existing definitions. To filter out irrelevant information, an AI-assisted selection process was used. In addition, we conducted bibliometric analyses and quality control procedures to assess content and accuracy. Overall, this approach allowed us to develop a robust and reliable database that serves as a valuable resource for stakeholders interested in co-creation. Results: The final version of the database included 13,501 papers, which are indexed in Zenodo and accessible in an open-access downloadable format. The quality assessment revealed that 20.3\% (140/688) of the database likely contained irrelevant material, whereas the methodology captured 91\% (58/64) of the relevant literature. Participatory and variations of the term co-creation were the most frequent terms in the title and abstracts of included literature. The predominant source journals included health sciences, sustainability, environmental sciences, medical research, and health services research. Conclusions: This study produced a high-quality, open-access database about co-creation. The study demonstrates that it is possible to perform a systematic review selection process on a fragmented concept using human-AI collaboration. Our unified concept of co-creation includes the co-approaches (co-creation, co-design, and co-production), forms of participatory research, and user involvement. Our analysis of authorship, citations, and source landscape highlights the potential lack of collaboration among co-creation researchers and underscores the need for future investigation into the different research methodologies. The database provides a resource for relevant literature and can support rapid literature reviews about co-creation. It also offers clarity about the current co-creation landscape and helps to address barriers that researchers may face when seeking evidence about co-creation. ", doi="10.2196/45059", url="https://www.jmir.org/2023/1/e45059", url="http://www.ncbi.nlm.nih.gov/pubmed/37463024" } @Article{info:doi/10.2196/41388, author="Tornberg, Haley and Moezinia, Carine and Wei, Chapman and Bernstein, A. Simone and Wei, Chaplin and Al-Beyati, Refka and Quan, Theodore and Diemert, J. David", title="Assessment of the Dissemination of COVID-19--Related Articles Across Social Media: Altmetrics Study", journal="JMIR Form Res", year="2023", month="Jul", day="12", volume="7", pages="e41388", keywords="Altmetric", keywords="COVID-19", keywords="citation", keywords="dissemination", keywords="information spread", keywords="impact factor", keywords="information", keywords="social media", keywords="bibliometric", keywords="scientometric", keywords="health professional", keywords="Twitter", keywords="database", keywords="data", abstract="Background: The use of social media assists in the distribution of information about COVID-19 to the general public and health professionals. Alternative-level metrics (ie, Altmetrics) is an alternative method to traditional bibliometrics that assess the extent of dissemination of a scientific article on social media platforms. Objective: Our study objective was to characterize and compare traditional bibliometrics (citation count) with newer metrics (Altmetric Attention Score [AAS]) of the top 100 Altmetric-scored articles on COVID-19. Methods: The top 100 articles with the highest AAS were identified using the Altmetric explorer in May 2020. AAS, journal name, and mentions from various social media platforms (Twitter, Facebook, Wikipedia, Reddit, Mendeley, and Dimension) were collected for each article. Citation counts were collected from the Scopus database. Results: The median AAS and citation count were 4922.50 and 24.00, respectively. TheNew England Journal of Medicine published the most articles (18/100, 18\%). Twitter was the most frequently used social media platform with 985,429 of 1,022,975 (96.3\%) mentions. Positive correlations were observed between AAS and citation count (r2=0.0973; P=.002). Conclusions: Our research characterized the top 100 COVID-19--related articles by AAS in the Altmetric database. Altmetrics could complement traditional citation count when assessing the dissemination of an article regarding COVID-19. International Registered Report Identifier (IRRID): RR2-10.2196/21408 ", doi="10.2196/41388", url="https://formative.jmir.org/2023/1/e41388", url="http://www.ncbi.nlm.nih.gov/pubmed/37343075" } @Article{info:doi/10.2196/46328, author="Park, Woo Han and Yoon, Young Ho", title="Global COVID-19 Policy Engagement With Scientific Research Information: Altmetric Data Study", journal="J Med Internet Res", year="2023", month="Jun", day="29", volume="25", pages="e46328", keywords="altmetrics", keywords="government policy report", keywords="citation analysis", keywords="COVID-19", keywords="World Health Organization", keywords="WHO", keywords="COVID-19 research", keywords="online citation network", keywords="policy domains", abstract="Background: Previous studies on COVID-19 scholarly articles have primarily focused on bibliometric characteristics, neglecting the identification of institutional actors that cite recent scientific contributions related to COVID-19 in the policy domain, and their locations. Objective: The purpose of this study was to assess the online citation network and knowledge structure of COVID-19 research across policy domains over 2 years from January 2020 to January 2022, with a particular emphasis on geographical frequency. Two research questions were addressed. The first question was related to who has been the most active in policy engagement with science and research information sharing during the COVID-19 pandemic, particularly in terms of countries and organization types. The second question was related to whether there are significant differences in the types of coronavirus research shared among countries and continents. Methods: The Altmetric database was used to collect policy report citations of scientific articles for 3 topic terms (COVID-19, COVID-19 vaccine, and COVID-19 variants). Altmetric provides the URLs of policy agencies that have cited COVID-19 research. The scientific articles used for Altmetric citations are extracted from journals indexed by PubMed. The numbers of COVID-19, COVID-19 vaccine, and COVID-19 variant research outputs between January 1, 2020, and January 31, 2022, were 216,787, 16,748, and 2777, respectively. The study examined the frequency of citations based on policy institutional domains, such as intergovernmental organizations, national and domestic governmental organizations, and nongovernmental organizations (think tanks and academic institutions). Results: The World Health Organization (WHO) stood out as the most notable institution citing COVID-19--related research outputs. The WHO actively sought and disseminated information regarding the COVID-19 pandemic. The COVID-19 vaccine citation network exhibited the most extensive connections in terms of degree centrality, 2-local eigenvector centrality, and eigenvector centrality among the 3 key terms. The Netherlands, the United States, the United Kingdom, and Australia were the countries that sought and shared the most information on COVID-19 vaccines, likely due to their high numbers of COVID-19 cases. Developing nations, although gaining quicker access to COVID-19 vaccine information, appeared to be relatively isolated from the enriched COVID-19 pandemic content in the global network. Conclusions: The global scientific network ecology during the COVID-19 pandemic revealed distinct types of links primarily centered around the WHO. Western countries demonstrated effective networking practices in constructing these networks. The prominent position of the key term ``COVID-19 vaccine'' demonstrates that nation-states align with global authority regardless of their national contexts. In summary, the citation networking practices of policy agencies have the potential to uncover the global knowledge distribution structure as a proxy for the networking strategy employed during a pandemic. ", doi="10.2196/46328", url="https://www.jmir.org/2023/1/e46328", url="http://www.ncbi.nlm.nih.gov/pubmed/37384384" } @Article{info:doi/10.2196/46014, author="Wang, Jingjing and Liang, Yiqing and Cao, Songmei and Cai, Peixuan and Fan, Yimeng", title="Application of Artificial Intelligence in Geriatric Care: Bibliometric Analysis", journal="J Med Internet Res", year="2023", month="Jun", day="23", volume="25", pages="e46014", keywords="artificial intelligence", keywords="older adults", keywords="geriatric care", keywords="bibliometric analysis", abstract="Background: Artificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to understand the status of development and main research hotspots and identify the main contributors and their relationships in the application of AI in geriatric care via bibliometric analysis. Objective: Using bibliometric analysis, this study aims to examine the current research hotspots and collaborative networks in the application of AI in geriatric care over the past 23 years. Methods: The Web of Science Core Collection database was used as a source. All publications from inception to August 2022 were downloaded. The external characteristics of the publications were summarized through HistCite and the Web of Science. Keywords and collaborative networks were analyzed using VOSviewers and Citespace. Results: We obtained a total of 230 publications. The works originated in 499 institutions in 39 countries, were published in 124 journals, and were written by 1216 authors. Publications increased sharply from 2014 to 2022, accounting for 90.87\% (209/230) of all publications. The United States and the International Journal of Social Robotics had the highest number of publications on this topic. The 1216 authors were divided into 5 main clusters. Among the 230 publications, 4 clusters were modeled, including Alzheimer disease, aged care, acceptance, and the surveillance and treatment of diseases. Machine learning, deep learning, and rehabilitation had also become recent research hotspots. Conclusions: Research on the application of AI in geriatric care has developed rapidly. The development of research and cooperation among countries/regions and institutions are limited. In the future, strengthening the cooperation and communication between different countries/regions and institutions may further drive this field's development. This study provides researchers with the information necessary to understand the current state, collaborative networks, and main research hotspots of the field. In addition, our results suggest a series of recommendations for future research. ", doi="10.2196/46014", url="https://www.jmir.org/2023/1/e46014", url="http://www.ncbi.nlm.nih.gov/pubmed/37351923" } @Article{info:doi/10.2196/42901, author="Zhang, Ying and Liu, Xiaoyu and Qiao, Xiaofeng and Fan, Yubo", title="Characteristics and Emerging Trends in Research on Rehabilitation Robots from 2001 to 2020: Bibliometric Study", journal="J Med Internet Res", year="2023", month="May", day="31", volume="25", pages="e42901", keywords="rehabilitation robot", keywords="bibliometric analysis", keywords="interdisciplinary research", keywords="co-occurrence analysis", keywords="co-citation analysis", keywords="rehabilitation", abstract="Background: The past 2 decades have seen rapid development in the use of robots for rehabilitation. Research on rehabilitation robots involves interdisciplinary activities, making it a great challenge to obtain comprehensive insights in this research field. Objective: We performed a bibliometric study to understand the characteristics of research on rehabilitation robots and emerging trends in this field in the last 2 decades. Methods: Reports on the topic of rehabilitation robots published from January 1, 2001, to December 31, 2020, were retrieved from the Web of Science Core Collection on July 28, 2022. Document types were limited to ``article'' and ``meeting'' (excluding the ``review'' type), to ensure that our analysis of the evolution over time of this research had high validity. We used CiteSpace to conduct a co-occurrence and co-citation analysis and to visualize the characteristics of this research field and emerging trends. Landmark publications were identified using metrics such as betweenness centrality and burst strength. Results: Through data retrieval, cleaning, and deduplication, we retrieved 9287 publications and 110,619 references cited in these publications that were on the topic of rehabilitation robots and were published between 2001 and 2020. Results of the Mann-Kendall test indicated that the numbers of both publications (P<.001; St=175.0) and citations (P<.001; St=188.0) related to rehabilitation robots exhibited a significantly increasing yearly trend. The co-occurrence results revealed 120 categories connected with research on rehabilitation robots; we used these categories to determine research relationships. The co-citation results identified 169 co-citation clusters characterizing this research field and emerging trends in it. The most prominent label was ``soft robotic technology'' (the burst strength was 79.07), which has become a topic of great interest in rehabilitative recovery for both the upper and lower limbs. Additionally, task-oriented upper-limb training, control strategies for robot-assisted lower limb rehabilitation, and power in exoskeleton robots were topics of great interest in current research. Conclusions: Our work provides insights into research on rehabilitation robots, including its characteristics and emerging trends during the last 2 decades, providing a comprehensive understanding of this research field. ", doi="10.2196/42901", url="https://www.jmir.org/2023/1/e42901", url="http://www.ncbi.nlm.nih.gov/pubmed/37256670" } @Article{info:doi/10.2196/42292, author="Amusa, Babatunde Lateef and Twinomurinzi, Hossana and Phalane, Edith and Phaswana-Mafuya, Nancy Refilwe", title="Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda", journal="Interact J Med Res", year="2023", month="Mar", day="31", volume="12", pages="e42292", keywords="big data", keywords="bibliometrics", keywords="infectious disease", keywords="COVID-19", keywords="disease surveillance", keywords="disease", keywords="pandemic", keywords="data", keywords="surveillance", keywords="hotspot", keywords="epidemiology", keywords="social media", keywords="utility", keywords="electronic health records", abstract="Background: Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling. Objective: The aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology. Methods: Bibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents. Results: The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes. Conclusions: Proposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology. ", doi="10.2196/42292", url="https://www.i-jmr.org/2023/1/e42292", url="http://www.ncbi.nlm.nih.gov/pubmed/36913554" } @Article{info:doi/10.2196/39021, author="Huang, Austin and Huang, Y. Kevin and Kim, Jung Soo", title="Retractions in Dermatology Literature Between 1982 and 2022: Cross-sectional Study", journal="JMIR Dermatol", year="2023", month="Mar", day="28", volume="6", pages="e39021", keywords="publication", keywords="retraction", keywords="bibliometrics", keywords="dermatology", doi="10.2196/39021", url="https://derma.jmir.org/2023/1/e39021", url="http://www.ncbi.nlm.nih.gov/pubmed/37632934" } @Article{info:doi/10.2196/35568, author="{\vS}uster, Simon and Baldwin, Timothy and Lau, Han Jey and Jimeno Yepes, Antonio and Martinez Iraola, David and Otmakhova, Yulia and Verspoor, Karin", title="Automating Quality Assessment of Medical Evidence in Systematic Reviews: Model Development and Validation Study", journal="J Med Internet Res", year="2023", month="Mar", day="13", volume="25", pages="e35568", keywords="critical appraisal", keywords="evidence synthesis", keywords="systematic reviews", keywords="bias detection", keywords="automated quality assessment", abstract="Background: Assessment of the quality of medical evidence available on the web is a critical step in the preparation of systematic reviews. Existing tools that automate parts of this task validate the quality of individual studies but not of entire bodies of evidence and focus on a restricted set of quality criteria. Objective: We proposed a quality assessment task that provides an overall quality rating for each body of evidence (BoE), as well as finer-grained justification for different quality criteria according to the Grading of Recommendation, Assessment, Development, and Evaluation formalization framework. For this purpose, we constructed a new data set and developed a machine learning baseline system (EvidenceGRADEr). Methods: We algorithmically extracted quality-related data from all summaries of findings found in the Cochrane Database of Systematic Reviews. Each BoE was defined by a set of population, intervention, comparison, and outcome criteria and assigned a quality grade (high, moderate, low, or very low) together with quality criteria (justification) that influenced that decision. Different statistical data, metadata about the review, and parts of the review text were extracted as support for grading each BoE. After pruning the resulting data set with various quality checks, we used it to train several neural-model variants. The predictions were compared against the labels originally assigned by the authors of the systematic reviews. Results: Our quality assessment data set, Cochrane Database of Systematic Reviews Quality of Evidence, contains 13,440 instances, or BoEs labeled for quality, originating from 2252 systematic reviews published on the internet from 2002 to 2020. On the basis of a 10-fold cross-validation, the best neural binary classifiers for quality criteria detected risk of bias at 0.78 F1 (P=.68; R=0.92) and imprecision at 0.75 F1 (P=.66; R=0.86), while the performance on inconsistency, indirectness, and publication bias criteria was lower (F1 in the range of 0.3-0.4). The prediction of the overall quality grade into 1 of the 4 levels resulted in 0.5 F1. When casting the task as a binary problem by merging the Grading of Recommendation, Assessment, Development, and Evaluation classes (high+moderate vs low+very low-quality evidence), we attained 0.74 F1. We also found that the results varied depending on the supporting information that is provided as an input to the models. Conclusions: Different factors affect the quality of evidence in the context of systematic reviews of medical evidence. Some of these (risk of bias and imprecision) can be automated with reasonable accuracy. Other quality dimensions such as indirectness, inconsistency, and publication bias prove more challenging for machine learning, largely because they are much rarer. This technology could substantially reduce reviewer workload in the future and expedite quality assessment as part of evidence synthesis. ", doi="10.2196/35568", url="https://www.jmir.org/2023/1/e35568", url="http://www.ncbi.nlm.nih.gov/pubmed/36722350" } @Article{info:doi/10.2196/42789, author="Macri, Carmelo and Bacchi, Stephen and Teoh, Chieh Sheng and Lim, Yin Wan and Lam, Lydia and Patel, Sandy and Slee, Mark and Casson, Robert and Chan, WengOnn", title="Evaluating the Ability of Open-Source Artificial Intelligence to Predict Accepting-Journal Impact Factor and Eigenfactor Score Using Academic Article Abstracts: Cross-sectional Machine Learning Analysis", journal="J Med Internet Res", year="2023", month="Mar", day="7", volume="25", pages="e42789", keywords="journal impact factor", keywords="artificial intelligence", keywords="ophthalmology", keywords="radiology", keywords="neurology", keywords="eye", keywords="neuroscience", keywords="impact factor", keywords="research quality", keywords="journal recommender", keywords="publish", keywords="open source", keywords="predict", keywords="machine learning", keywords="academic journal", keywords="scientometric", keywords="scholarly literature", abstract="Background: Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. Objective: We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts. Methods: PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms ``ophthalmology,'' ``radiology,'' and ``neurology.'' Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile. Results: There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0\%, followed by an accuracy of 71.6\% for XGBoost and 65.4\% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6\%, followed by an accuracy of 71.8\% for XGBoost and 65.3\% for logistic regression. Conclusions: Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems. ", doi="10.2196/42789", url="https://www.jmir.org/2023/1/e42789", url="http://www.ncbi.nlm.nih.gov/pubmed/36881455" } @Article{info:doi/10.2196/43089, author="Delardas, Orestis and Giannos, Panagiotis", title="How COVID-19 Affected the Journal Impact Factor of High Impact Medical Journals: Bibliometric Analysis", journal="J Med Internet Res", year="2022", month="Dec", day="21", volume="24", number="12", pages="e43089", keywords="COVID-19", keywords="journal impact factor", keywords="scientometrics", keywords="bibliometrics", keywords="infometrics", keywords="journal", keywords="assessment", keywords="research", keywords="resources", keywords="medical journal", keywords="literature", keywords="database", keywords="community", keywords="behavior", abstract="Background: Journal impact factor (IF) is the leading method of scholarly assessment in today's research world, influencing where scholars submit their research and where funders distribute their resources. COVID-19, one of the most serious health crises, resulted in an unprecedented surge of publications across all areas of knowledge. An important question is whether COVID-19 affected the gold standard of scholarly assessment. Objective: In this paper, we aimed to comprehensively compare the productivity trends of COVID-19 and non--COVID-19 literature as well as track their evolution and scholarly impact across 3 consecutive calendar years. Methods: We took as an example 6 high-impact medical journals (Annals of Internal Medicine [Annals], The British Medical Journal [The BMJ], Journal of the American Medical Association [JAMA], The Lancet, Nature Medicine [NatMed], and The New England Journal of Medicine [NEJM]) and searched the literature using the Web of Science database for manuscripts published between January 1, 2019, and December 31, 2021. To assess the effect of COVID-19 and non--COVID-19 literature in their scholarly impact, we calculated their annual IFs and percentage changes. Thereafter, we estimated the citation probability of COVID-19 and non--COVID-19 publications along with their rates of publication and citation by journal. Results: A significant increase in IF change for manuscripts including COVID-19 published from 2019 to 2020 (P=.002; Annals: 283\%; The BMJ: 199\%; JAMA: 208\%; The Lancet: 392\%; NatMed: 111\%; and NEJM: 196\%) and to 2021 (P=.007; Annals: 41\%; The BMJ: 90\%; JAMA: 6\%; The Lancet: 22\%; NatMed: 53\%; and NEJM: 72\%) was seen, against non--COVID-19 ones. The likelihood of highly cited publications was significantly increased in COVID-19 manuscripts between 2019 and 2021 (Annals: z=3.4, P<.001; The BMJ: z=4.0, P<.001; JAMA: z=3.8, P<.001; The Lancet: z=3.5, P<.001; NatMed: z=5.2, P<.001; and NEJM: z=4.7, P<.001). The publication and citation rates of COVID-19 publications followed a positive trajectory, as opposed to non--COVID-19. The citation rate for COVID-19 publications peaked by the second quarter of 2020 while that of the publication rate approximately a year later. Conclusions: The rapid surge of COVID-19 publications emphasized the capacity of scientific communities to respond against a global health emergency, yet inflated IFs create ambiguity as benchmark tools for assessing scholarly impact. The immediate implication is a loss in value of and trust in journal IFs as metrics of research and scientific rigor perceived by academia and society. Loss of confidence toward procedures employed by highly reputable publishers may incentivize authors to exploit the publication process by monopolizing their research on COVID-19 and encourage them to publish in journals of predatory behavior. ", doi="10.2196/43089", url="https://www.jmir.org/2022/12/e43089", url="http://www.ncbi.nlm.nih.gov/pubmed/36454727" } @Article{info:doi/10.2196/42891, author="Karystianis, George and Lukmanjaya, Wilson and Simpson, Paul and Schofield, Peter and Ginnivan, Natasha and Nenadic, Goran and van Leeuwen, Marina and Buchan, Iain and Butler, Tony", title="An Analysis of PubMed Abstracts From 1946 to 2021 to Identify Organizational Affiliations in Epidemiological Criminology: Descriptive Study", journal="Interact J Med Res", year="2022", month="Dec", day="5", volume="11", number="2", pages="e42891", keywords="epidemiological criminology", keywords="PubMed", keywords="offenders", keywords="justice health", keywords="affiliations", keywords="health database", keywords="research output", keywords="criminology", keywords="publication", keywords="open research", keywords="research promotion", keywords="epidemiology research", keywords="research database", abstract="Background: Epidemiological criminology refers to health issues affecting incarcerated and nonincarcerated offender populations, a group recognized as being challenging to conduct research with. Notwithstanding this, an urgent need exists for new knowledge and interventions to improve health, justice, and social outcomes for this marginalized population. Objective: To better understand research outputs in the field of epidemiological criminology, we examined the lead author's affiliation by analyzing peer-reviewed published outputs to determine countries and organizations (eg, universities, governmental and nongovernmental organizations) responsible for peer-reviewed publications. Methods: We used a semiautomated approach to examine the first-author affiliations of 23,904 PubMed epidemiological studies related to incarcerated and offender populations published in English between 1946 and 2021. We also mapped research outputs to the World Justice Project Rule of Law Index to better understand whether there was a relationship between research outputs and the overall standard of a country's justice system. Results: Nordic countries (Sweden, Norway, Finland, and Denmark) had the highest research outputs proportional to their incarcerated population, followed by Australia. University-affiliated first authors comprised 73.3\% of published articles, with the Karolinska Institute (Sweden) being the most published, followed by the University of New South Wales (Australia). Government-affiliated first authors were on 8.9\% of published outputs, and prison-affiliated groups were on 1\%. Countries with the lowest research outputs also had the lowest scores on the Rule of Law Index. Conclusions: This study provides important information on who is publishing research in the epidemiological criminology field. This has implications for promoting research diversity, independence, funding equity, and partnerships between universities and government departments that control access to incarcerated and offending populations. ", doi="10.2196/42891", url="https://www.i-jmr.org/2022/2/e42891", url="http://www.ncbi.nlm.nih.gov/pubmed/36469411" } @Article{info:doi/10.2196/42185, author="Tang, Ri and Zhang, Shuyi and Ding, Chenling and Zhu, Mingli and Gao, Yuan", title="Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis", journal="J Med Internet Res", year="2022", month="Nov", day="30", volume="24", number="11", pages="e42185", keywords="intensive care medicine", keywords="artificial intelligence", keywords="bibliometric analysis", keywords="machine learning", keywords="sepsis", abstract="Background: Interest in critical care--related artificial intelligence (AI) research is growing rapidly. However, the literature is still lacking in comprehensive bibliometric studies that measure and analyze scientific publications globally. Objective: The objective of this study was to assess the global research trends in AI in intensive care medicine based on publication outputs, citations, coauthorships between nations, and co-occurrences of author keywords. Methods: A total of 3619 documents published until March 2022 were retrieved from the Scopus database. After selecting the document type as articles, the titles and abstracts were checked for eligibility. In the final bibliometric study using VOSviewer, 1198 papers were included. The growth rate of publications, preferred journals, leading research countries, international collaborations, and top institutions were computed. Results: The number of publications increased steeply between 2018 and 2022, accounting for 72.53\% (869/1198) of all the included papers. The United States and China contributed to approximately 55.17\% (661/1198) of the total publications. Of the 15 most productive institutions, 9 were among the top 100 universities worldwide. Detecting clinical deterioration, monitoring, predicting disease progression, mortality, prognosis, and classifying disease phenotypes or subtypes were some of the research hot spots for AI in patients who are critically ill. Neural networks, decision support systems, machine learning, and deep learning were all commonly used AI technologies. Conclusions: This study highlights popular areas in AI research aimed at improving health care in intensive care units, offers a comprehensive look at the research trend in AI application in the intensive care unit, and provides an insight into potential collaboration and prospects for future research. The 30 articles that received the most citations were listed in detail. For AI-based clinical research to be sufficiently convincing for routine critical care practice, collaborative research efforts are needed to increase the maturity and robustness of AI-driven models. ", doi="10.2196/42185", url="https://www.jmir.org/2022/11/e42185", url="http://www.ncbi.nlm.nih.gov/pubmed/36449345" } @Article{info:doi/10.2196/40011, author="San Torcuato, Maider and Bautista-Puig, N{\'u}ria and Arrizabalaga, Olatz and M{\'e}ndez, Eva", title="Tracking Openness and Topic Evolution of COVID-19 Publications January 2020-March 2021: Comprehensive Bibliometric and Topic Modeling Analysis", journal="J Med Internet Res", year="2022", month="Oct", day="3", volume="24", number="10", pages="e40011", keywords="COVID-19", keywords="open access", keywords="OA", keywords="SARS-CoV-2", keywords="scholarly communication", keywords="topic modeling", keywords="research", keywords="dissemination", keywords="accessibility", keywords="scientometry", keywords="publications", keywords="communication", keywords="research topics", abstract="Background: The COVID-19 outbreak highlighted the importance of rapid access to research. Objective: The aim of this study was to investigate research communication related to COVID-19, the level of openness of papers, and the main topics of research into this disease. Methods: Open access (OA) uptake (typologies, license use) and the topic evolution of publications were analyzed from the start of the pandemic (January 1, 2020) until the end of a year of widespread lockdown (March 1, 2021). Results: The sample included 95,605 publications; 94.1\% were published in an OA form, 44\% of which were published as Bronze OA. Among these OA publications, 42\% do not have a license, which can limit the number of citations and thus the impact. Using a topic modeling approach, we found that articles in Hybrid and Green OA publications are more focused on patients and their effects, whereas the strategy to combat the pandemic adopted by different countries was the main topic of articles selecting publication via the Gold OA route. Conclusions: Although OA scientific production has increased, some weaknesses in OA practice, such as lack of licensing or under-researched topics, still hold back its effective use for further research. ", doi="10.2196/40011", url="https://www.jmir.org/2022/10/e40011", url="http://www.ncbi.nlm.nih.gov/pubmed/36190742" } @Article{info:doi/10.2196/39365, author="Ottwell, Ryan and Hightower, Brooke and Failla, Olivia and Snider, Kelsey and Corcoran, Adam and Hartwell, Micah and Vassar, Matt", title="An Evaluation of Primary Studies Published in Predatory Journals Included in Systematic Reviews From High-Impact Dermatology Journals: Cross-sectional Study", journal="JMIR Dermatol", year="2022", month="Sep", day="14", volume="5", number="3", pages="e39365", keywords="predatory journals", keywords="systematic review", keywords="general dermatology", keywords="dermatology", keywords="publishing", keywords="publications", keywords="journals", keywords="scientific communication", keywords="data", keywords="quality", keywords="meta-analysis", keywords="peer review", keywords="primary studies", keywords="research", keywords="evidence synthesis", keywords="articles", abstract="Background: Predatory publishing is a deceptive form of publishing that uses unethical business practices, minimal to no peer review processes, or limited editorial oversight to publish articles. It may be problematic to our highest standard of scientific evidence---systematic reviews---through the inclusion of poor-quality and unusable data, which could mislead results, challenge outcomes, and undermine confidence. Thus, there is a growing concern surrounding the effects predatory publishing may have on scientific research and clinical decision-making. Objective: The objective of this study was to evaluate whether systematic reviews published in top dermatology journals contain primary studies published in suspected predatory journals (SPJs). Methods: We searched PubMed for systematic reviews published in the top five dermatology journals (determined by 5-year h-indices) between January 1, 2019, and May 24, 2021. Primary studies were extracted from each systematic review, and the publishing journal of these primary studies was cross-referenced using Beall's List and the Directory of Open Access Journals. Screening and data extraction were performed in a masked, duplicate fashion. We performed chi-square tests to determine possible associations between a systematic review's inclusion of a primary study published in a SPJ and particular study characteristics. Results: Our randomized sample included 100 systematic reviews, of which 31 (31\%) were found to contain a primary study published in a SPJ. Of the top five dermatology journals, the Journal of the American Academy of Dermatology had the most systematic reviews containing a primary study published in an SPJ. Systematic reviews containing a meta-analysis or registered protocol were significantly less likely to contain a primary study published in a SPJ. No statistically significant associations were found between other study characteristics. Conclusions: Studies published in SPJs are commonly included as primary studies in systematic reviews published in high-impact dermatology journals. Future research is needed to investigate the effects of including suspected predatory publications in scientific research. ", doi="10.2196/39365", url="https://derma.jmir.org/2022/3/e39365", url="http://www.ncbi.nlm.nih.gov/pubmed/37632887" } @Article{info:doi/10.2196/39201, author="Huang, Austin and Zhu, Harrison and Zhou, Kelvin and Kirby, Parker R. and Dasari, Nina and Calderara, A. Gianmarco and Cordova, Kathryn and Sorensen, Ryan and Bhatnagar, Anshul and Kim, Jung Soo", title="Social Media Impact of Articles Published by Dermatology Residents During Medical School: Cross-sectional Study", journal="JMIR Dermatol", year="2022", month="Sep", day="12", volume="5", number="3", pages="e39201", keywords="Altmetric score", keywords="bibliometrics", keywords="social media", keywords="dermatology", keywords="resident", keywords="medical student", keywords="publication", keywords="citation", keywords="Altmetric", keywords="research quality", keywords="publish", keywords="impact factor", keywords="Scientometrics", abstract="Background: The Altmetric score (AS) is a novel measure of publication impact that is calculated by the number of mentions across various social media websites. This method may have advantages over traditional bibliometrics in the context of research by medical students. Objective: This study aimed to determine whether dermatology matriculants who graduated from higher-ranked medical schools published more articles with greater impact (ie, a higher AS) than those from lower-ranked institutions. Methods: A PubMed search for articles published by dermatology residents who started medical school in 2020 was conducted. Demographic information and Altmetric data were collected, and medical schools were sorted according to US News' top-25 and non--top-25 categories. Results: Residents who completed their medical training at a top-25 institution published more papers (mean 4.93, SD 4.18 vs mean 3.11, SD 3.32; P<.001) and accrued a significantly higher total AS (mean 67.9, SD 160 vs mean 22.9, SD 75.9; P<.001) and average AS (mean 13.1, SD 23.7 vs mean 6.71, SD 32.3; P<.001) per article than those who graduated from non--top-25 schools. Conclusions: Our results indicate that students in top-25 schools may have greater access to research resources and opportunities. With a pass/fail United States Medical Licensing Examination Step 1 exam that may increasingly shift focus toward scholarly output from medical students, further discussion on how to create a more equitable dermatology match is essential. ", doi="10.2196/39201", url="https://derma.jmir.org/2022/3/e39201", url="http://www.ncbi.nlm.nih.gov/pubmed/37632895" } @Article{info:doi/10.2196/39948, author="Hassan, Waseem and Zafar, Mehreen and Teixeira da Rocha, Batista Joao", title="The Leading Authors in Three High Impact Dermatology Journals", journal="JMIR Dermatol", year="2022", month="Aug", day="23", volume="5", number="3", pages="e39948", keywords="JAAD", keywords="Scopus", keywords="bibliometry", keywords="dermatology", keywords="research", keywords="publications", keywords="articles", keywords="bibliometrics", doi="10.2196/39948", url="https://derma.jmir.org/2022/3/e39948", url="http://www.ncbi.nlm.nih.gov/pubmed/37632901" } @Article{info:doi/10.2196/38591, author="Tran, Xuan Bach and Nguyen, Hoang Long and Nguyen, Si Hao Anh and Vu, Thi Thuc Minh and Do, Linh Anh and Nguyen, Khanh Lien Thi and Kim, Ngoc Nga Thanh and Trinh, Hong Trang Thu and Latkin, Carl and Ho, H. Cyrus S. and Ho, M. Roger C.", title="Evolution of Interdisciplinary Approaches Among Research-Oriented Universities in Vietnam Toward a Modern Industrial Economy: Exploratory Study", journal="Interact J Med Res", year="2022", month="Aug", day="17", volume="11", number="2", pages="e38591", keywords="research", keywords="performance", keywords="productivity", keywords="scientometric", keywords="Vietnam", keywords="Asia", keywords="metric", keywords="pattern", keywords="journal", keywords="publication", keywords="publishing", keywords="output", keywords="science", keywords="scientific", abstract="Background: Vietnam's 2045 development plan requires thorough reforms in science and technology, which underlines the role of research-oriented universities in generating and transforming knowledge. Understanding the current research performance and productivity in Vietnam is important for exploiting future agendas. Objective: This study aims to explore the growth patterns and collaborations in the scientific publications of Vietnam. Methods: Data on documents in the Web of Science Core Collection database were searched and extracted to examine the research performance in Vietnam. Publication growth patterns in both quantity and quality were examined. The evolution of research disciplines and collaboration networks were also analyzed. Trends in the growth in the number of publications, citations, and average citations per publication between 1966 and 2020 were presented. Temporal tendencies of the 10 most productive research areas in each period were illustrated. VOSviewer software was used to analyze the discipline network, country network, and institution networks. The trends and the geographical distribution of the number of publications and citations were analyzed. Results: A total of 62,752 documents in 8354 different sources from 1966 to 2020 were retrieved. A substantial growth was observed in the Vietnamese scientific output during this period, which was mainly research with international collaboration. Natural sciences such as mathematics, materials science, and physics were the top 3 most productive research fields during 1966-2020 in Vietnam, followed by experimental research fields such as multidisciplinary sciences, plant sciences, public, environmental, and occupational health. In 1966-2020, there was the emergence of multidisciplinary research--oriented universities in both public and private sectors along with a significant increase in the number of interdisciplinary and multidisciplinary publications. Although the scientific quality has improved, these publications are still of mostly medium quality as they are concentrated in middle-ranking journals. Conclusions: Our study highlights the notable growth in research performance in terms of both quality and quantity in Vietnam from 1966 to 2020. Building multidisciplinary and interdisciplinary research agenda, developing networks of local and international researchers for addressing specific local issues, improving the participation of private sectors, and developing science and technology mechanisms are critical for boosting the research productivity in Vietnam. ", doi="10.2196/38591", url="https://www.i-jmr.org/2022/2/e38591", url="http://www.ncbi.nlm.nih.gov/pubmed/35976182" } @Article{info:doi/10.2196/35276, author="Otridge, Jeremy and Ogden, L. Cynthia and Bernstein, T. Kyle and Knuth, Martha and Fishman, Julie and Brooks, T. John", title="Publication and Impact of Preprints Included in the First 100 Editions of the CDC COVID-19 Science Update: Content Analysis", journal="JMIR Public Health Surveill", year="2022", month="Jul", day="15", volume="8", number="7", pages="e35276", keywords="preprints", keywords="preprint", keywords="publishing", keywords="publish", keywords="bioRxiv", keywords="medRxiv", keywords="Centers for Disease Control and Prevention", keywords="CDC", keywords="preprint server", keywords="public health", keywords="health information", keywords="COVID-19", keywords="pandemic", keywords="publication", keywords="Altmetric attention score", keywords="Altmetric", keywords="attention score", keywords="citation count", keywords="citation", keywords="science update", keywords="decision-making", abstract="Background: Preprints are publicly available manuscripts posted to various servers that have not been peer reviewed. Although preprints have existed since 1961, they have gained increased popularity during the COVID-19 pandemic due to the need for immediate, relevant information. Objective: The aim of this study is to evaluate the publication rate and impact of preprints included in the Centers for Disease Control and Prevention (CDC) COVID-19 Science Update and assess the performance of the COVID-19 Science Update team in selecting impactful preprints. Methods: All preprints in the first 100 editions (April 1, 2020, to July 30, 2021) of the Science Update were included in the study. Preprints that were not published were categorized as ``unpublished preprints.'' Preprints that were subsequently published exist in 2 versions (in a peer-reviewed journal and on the original preprint server), which were analyzed separately and referred to as ``peer-reviewed preprint'' and ``original preprint,'' respectively. Time to publish was the time interval between the date on which a preprint was first posted and the date on which it was first available as a peer-reviewed article. Impact was quantified by Altmetric Attention Score and citation count for all available manuscripts on August 6, 2021. Preprints were analyzed by publication status, publication rate, preprint server, and time to publication. Results: Of the 275 preprints included in the CDC COVID-19 Science Update during the study period, most came from three servers: medRxiv (n=201, 73.1\%), bioRxiv (n=41, 14.9\%), and SSRN (n=25, 9.1\%), with 8 (2.9\%) coming from other sources. Additionally, 152 (55.3\%) were eventually published. The median time to publish was 2.3 (IQR 1.4-3.7). When preprints posted in the last 2.3 months were excluded (to account for the time to publish), the publication rate was 67.8\%. Moreover, 76 journals published at least one preprint from the CDC COVID-19 Science Update, and 18 journals published at least three. The median Altmetric Attention Score for unpublished preprints (n=123, 44.7\%) was 146 (IQR 22-552) with a median citation count of 2 (IQR 0-8); for original preprints (n=152, 55.2\%), these values were 212 (IQR 22-1164) and 14 (IQR 2-40), respectively; for peer-review preprints, these values were 265 (IQR 29-1896) and 19 (IQR 3-101), respectively. Conclusions: Prior studies of COVID-19 preprints found publication rates between 5.4\% and 21.1\%. Preprints included in the CDC COVID-19 Science Update were published at a higher rate than overall COVID-19 preprints, and those that were ultimately published were published within months and received higher attention scores than unpublished preprints. These findings indicate that the Science Update process for selecting preprints had a high fidelity in terms of their likelihood to be published and their impact. The incorporation of high-quality preprints into the CDC COVID-19 Science Update improves this activity's capacity to inform meaningful public health decision-making. ", doi="10.2196/35276", url="https://publichealth.jmir.org/2022/7/e35276", url="http://www.ncbi.nlm.nih.gov/pubmed/35544426" } @Article{info:doi/10.2196/35816, author="Yang, Keng and Hu, Yekang and Qi, Hanying", title="Digital Health Literacy: Bibliometric Analysis", journal="J Med Internet Res", year="2022", month="Jul", day="6", volume="24", number="7", pages="e35816", keywords="digital health literacy", keywords="eHealth", keywords="digital divide", keywords="bibliometrics", keywords="VOSviewer", keywords="CiteSpace", abstract="Background: Digital health is growing at a rapid pace, and digital health literacy has attracted increasing attention from the academic community. Objective: The purposes of this study are to conduct a systematic bibliometric analysis on the field of digital health literacy and to understand the research context and trends in this field. Methods: Methods: A total of 1955 scientific publications were collected from the Web of Science core collection. Institutional co-operation, journal co-citation, theme bursting, keyword co-occurrence, author co-operation, author co-citation, literature co-citation, and references in the field of digital health literacy were analyzed using the VOSviewer and CiteSpace knowledge mapping tools. Results: The results demonstrate that the United States has the highest number of publications and citations in this field. The University of California System was first in terms of institutional contributions. The Journal of Medical Internet Research led in the number of publications, citations, and co-citations. Research areas of highly cited articles in the field of digital health literacy mainly include the definition and scale of health literacy, health literacy and health outcomes, health literacy and the digital divide, and the influencing factors of health literacy. Conclusions: We summarized research progress in the field of digital health literacy and reveal the context, trends, and trending topics of digital health literacy research through statistical analysis and network visualization. We found that digital health literacy has a significant potential to improve health outcomes, bridge the digital divide, and reduce health inequalities. Our work can serve as a fundamental reference and directional guide for future research in this field. ", doi="10.2196/35816", url="https://www.jmir.org/2022/7/e35816", url="http://www.ncbi.nlm.nih.gov/pubmed/35793141" } @Article{info:doi/10.2196/37324, author="Menke, Joe and Eckmann, Peter and Ozyurt, Burak Ibrahim and Roelandse, Martijn and Anderson, Nathan and Grethe, Jeffrey and Gamst, Anthony and Bandrowski, Anita", title="Establishing Institutional Scores With the Rigor and Transparency Index: Large-scale Analysis of Scientific Reporting Quality", journal="J Med Internet Res", year="2022", month="Jun", day="27", volume="24", number="6", pages="e37324", keywords="research reproducibility", keywords="rigor and transparency", keywords="reproducibility crisis", keywords="reporting transparency", keywords="science of science", keywords="research metric", keywords="data and code availability", keywords="cell line authentication", keywords="university ranking", abstract="Background: Improving rigor and transparency measures should lead to improvements in reproducibility across the scientific literature; however, the assessment of measures of transparency tends to be very difficult if performed manually. Objective: This study addresses the enhancement of the Rigor and Transparency Index (RTI, version 2.0), which attempts to automatically assess the rigor and transparency of journals, institutions, and countries using manuscripts scored on criteria found in reproducibility guidelines (eg, Materials Design, Analysis, and Reporting checklist criteria). Methods: The RTI tracks 27 entity types using natural language processing techniques such as Bidirectional Long Short-term Memory Conditional Random Field--based models and regular expressions; this allowed us to assess over 2 million papers accessed through PubMed Central. Results: Between 1997 and 2020 (where data were readily available in our data set), rigor and transparency measures showed general improvement (RTI 2.29 to 4.13), suggesting that authors are taking the need for improved reporting seriously. The top-scoring journals in 2020 were the Journal of Neurochemistry (6.23), British Journal of Pharmacology (6.07), and Nature Neuroscience (5.93). We extracted the institution and country of origin from the author affiliations to expand our analysis beyond journals. Among institutions publishing >1000 papers in 2020 (in the PubMed Central open access set), Capital Medical University (4.75), Yonsei University (4.58), and University of Copenhagen (4.53) were the top performers in terms of RTI. In country-level performance, we found that Ethiopia and Norway consistently topped the RTI charts of countries with 100 or more papers per year. In addition, we tested our assumption that the RTI may serve as a reliable proxy for scientific replicability (ie, a high RTI represents papers containing sufficient information for replication efforts). Using work by the Reproducibility Project: Cancer Biology, we determined that replication papers (RTI 7.61, SD 0.78) scored significantly higher (P<.001) than the original papers (RTI 3.39, SD 1.12), which according to the project required additional information from authors to begin replication efforts. Conclusions: These results align with our view that RTI may serve as a reliable proxy for scientific replicability. Unfortunately, RTI measures for journals, institutions, and countries fall short of the replicated paper average. If we consider the RTI of these replication studies as a target for future manuscripts, more work will be needed to ensure that the average manuscript contains sufficient information for replication attempts. ", doi="10.2196/37324", url="https://www.jmir.org/2022/6/e37324", url="http://www.ncbi.nlm.nih.gov/pubmed/35759334" } @Article{info:doi/10.2196/32728, author="Xu, Yantao and Jiang, Zixi and Kuang, Xinwei and Chen, Xiang and Liu, Hong", title="Research Trends in Immune Checkpoint Blockade for Melanoma: Visualization and Bibliometric Analysis", journal="J Med Internet Res", year="2022", month="Jun", day="27", volume="24", number="6", pages="e32728", keywords="melanoma", keywords="immune checkpoint blockade", keywords="bibliometric", keywords="research trends", keywords="dermatology", keywords="cancer", abstract="Background: Melanoma is one of the most life-threatening skin cancers; immune checkpoint blockade is widely used in the treatment of melanoma because of its remarkable efficacy. Objective: This study aimed to conduct a comprehensive bibliometric analysis of research conducted in recent decades on immune checkpoint blockade for melanoma, while exploring research trends and public interest in this topic. Methods: We summarized the articles in the Web of Science Core Collection on immune checkpoint blockade for melanoma in each year from 1999 to 2020. The R package bibliometrix was used for data extraction and visualization of the distribution of publication year and the top 10 core authors. Keyword citation burst analysis and cocitation networks were calculated with CiteSpace. A Gunn online world map was used to evaluate distribution by country and region. Ranking was performed using the Standard Competition Ranking method. Coauthorship analysis and co-occurrence were analyzed and visualized with VOSviewer. Results: After removing duplicates, a total of 9169 publications were included. The distribution of publications by year showed that the number of publications rose sharply from 2015 onwards and either reached a peak in 2020 or has yet to reach a peak. The geographical distribution indicated that there was a large gap between the number of publications in the United States and other countries. The coauthorship analysis showed that the 149 top institutions were grouped into 8 clusters, each covering approximately a single country, suggesting that international cooperation among institutions should be strengthened. The core author extraction revealed changes in the most prolific authors. The keyword analysis revealed clustering and top citation bursts. The cocitation analysis of references from 2010 to 2020 revealed the number of citations and the centrality of the top articles. Conclusions: This study revealed trends in research and public interest in immune checkpoint blockade for melanoma. Our findings suggest that the field is growing rapidly, has several core authors, and that the United States is taking the lead position. Moreover, cooperation between countries should be strengthened, and future research hot spots might focus on deeper exploration of drug mechanisms, prediction of treatment efficacy, prediction of adverse events, and new modes of administration, such as combination therapy, which may pave the way for further research. ", doi="10.2196/32728", url="https://www.jmir.org/2022/6/e32728", url="http://www.ncbi.nlm.nih.gov/pubmed/35759331" } @Article{info:doi/10.2196/37532, author="Zhao, Junqiang and Lu, Yi and Qian, Yong and Luo, Yuxin and Yang, Weihua", title="Emerging Trends and Research Foci in Artificial Intelligence for Retinal Diseases: Bibliometric and Visualization Study", journal="J Med Internet Res", year="2022", month="Jun", day="14", volume="24", number="6", pages="e37532", keywords="artificial intelligence", keywords="retinal disease", keywords="data visualization", keywords="bibliometric", keywords="citespace, VOSviewer", keywords="retinal", keywords="eye", keywords="visual impairment", abstract="Background: Patients with retinal diseases may exhibit serious complications that cause severe visual impairment owing to a lack of awareness of retinal diseases and limited medical resources. Understanding how artificial intelligence (AI) is used to make predictions and perform relevant analyses is a very active area of research on retinal diseases. In this study, the relevant Science Citation Index (SCI) literature on the AI of retinal diseases published from 2012 to 2021 was integrated and analyzed. Objective: The aim of this study was to gain insights into the overall application of AI technology to the research of retinal diseases from set time and space dimensions. Methods: Citation data downloaded from the Web of Science Core Collection database for AI in retinal disease publications from January 1, 2012, to December 31, 2021, were considered for this analysis. Information retrieval was analyzed using the online analysis platforms of literature metrology: Bibliometrc, CiteSpace V, and VOSviewer. Results: A total of 197 institutions from 86 countries contributed to relevant publications; China had the largest number and researchers from University College London had the highest H-index. The reference clusters of SCI papers were clustered into 12 categories. ``Deep learning'' was the cluster with the widest range of cocited references. The burst keywords represented the research frontiers in 2018-2021, which were ``eye disease'' and ``enhancement.'' Conclusions: This study provides a systematic analysis method on the literature regarding AI in retinal diseases. Bibliometric analysis enabled obtaining results that were objective and comprehensive. In the future, high-quality retinal image--forming AI technology with strong stability and clinical applicability will continue to be encouraged. ", doi="10.2196/37532", url="https://www.jmir.org/2022/6/e37532", url="http://www.ncbi.nlm.nih.gov/pubmed/35700021" } @Article{info:doi/10.2196/35747, author="Wu, Chieh-Chen and Huang, Chih-Wei and Wang, Yao-Chin and Islam, Md.Mohaimenul and Kung, Woon-Man and Weng, Yung-Ching and Su, Chun-Hsien", title="mHealth Research for Weight Loss, Physical Activity, and Sedentary Behavior: Bibliometric Analysis", journal="J Med Internet Res", year="2022", month="Jun", day="8", volume="24", number="6", pages="e35747", keywords="mobile health", keywords="weight loss", keywords="physical activity", keywords="sedentary behavior", keywords="bibliometric analysis", keywords="mHealth", keywords="weight", keywords="behavior", keywords="research", keywords="literature", keywords="bibliometric", keywords="journal", keywords="trend", keywords="app", abstract="Background: Research into mobile health (mHealth) technologies on weight loss, physical activity, and sedentary behavior has increased substantially over the last decade; however, no research has been published showing the research trend in this field. Objective: The purpose of this study was to provide a dynamic and longitudinal bibliometric analysis of recent trends of mHealth research for weight loss, physical activity, and sedentary behavior. Methods: A comprehensive search was conducted through Web of Science to retrieve all existing relevant documents published in English between January 1, 2010, and November 1, 2021. We developed appropriate research questions; based on the proven bibliometric approaches, a search strategy was formulated to screen the title for eligibility. Finally, we conducted bibliometric analyses to explore the growth rate of publications; publication patterns; and the most productive authors, institutions, and countries, and visualized the trends in the field using a keyword co-occurrence network. Results: The initial search identified 8739 articles, of which 1035 were included in the analyses. Our findings show an exponential growth trend in the number of annual publications of mHealth technology research in these fields. JMIR mHealth and uHealth (n=214, 20.67\%), Journal of Medical Internet Research (n=71, 6.86\%), and BMC Public Health (n=36, 3.47\%) were the top 3 journals, publishing higher numbers of articles. The United States remained the leading contributor in these areas (n=405, 39.13\%), followed by Australia (n=154, 14.87\%) and England (n=125, 12.07\%). Among the universities, the University of Sydney (n=36, 3.47\%) contributed the most mHealth technology research in these areas; however, Deakin University (n=25, 2.41\%) and the National University of Singapore (n=23, 2.22\%) were in the second and third positions, respectively. Conclusions: Although the number of papers published on mobile technologies for weight loss, physical activity, and sedentary behavior was initially low, there has been an overall increase in these areas in recent years. The findings of the study indicate that mobile apps and technologies have substantial potential to reduce weight, increase physical activity, and change sedentary behavior. Indeed, this study provides a useful overview of the publication trends and valuable guidance on future research directions and perspectives in this rapidly developing field. ", doi="10.2196/35747", url="https://www.jmir.org/2022/6/e35747", url="http://www.ncbi.nlm.nih.gov/pubmed/35675126" } @Article{info:doi/10.2196/37256, author="Cooper, R. Benjamin and Anderson, B. Jaclyn and Laughter, R. Melissa and Presley, L. Colby and Albrecht, Mark J. and Dellavalle, P. Robert", title="Top Skin-of-Color Publications in Dermatology", journal="JMIR Dermatol", year="2022", month="Jun", day="6", volume="5", number="2", pages="e37256", keywords="skin of color", keywords="dermatology", keywords="Web of Science", keywords="Altmetric", keywords="Altmetric Attention Score", keywords="decision-making", keywords="public attention", keywords="media", keywords="blogs", keywords="skin disorder", keywords="dermatologic conditions", keywords="online media", keywords="publication", keywords="citation", keywords="impact", keywords="health information", keywords="information exchange", keywords="education", doi="10.2196/37256", url="https://derma.jmir.org/2022/2/e37256", url="http://www.ncbi.nlm.nih.gov/pubmed/37632864" } @Article{info:doi/10.2196/28354, author="Sauvayre, Romy", title="Types of Errors Hiding in Google Scholar Data", journal="J Med Internet Res", year="2022", month="May", day="27", volume="24", number="5", pages="e28354", keywords="reference accuracy", keywords="database reliability", keywords="false positives", keywords="academic publication", keywords="research evaluation", keywords="scientometrics", keywords="citation analysis", doi="10.2196/28354", url="https://www.jmir.org/2022/5/e28354", url="http://www.ncbi.nlm.nih.gov/pubmed/35622395" } @Article{info:doi/10.2196/36086, author="Yeung, Kan Andy Wai and Kulnik, Tino Stefan and Parvanov, D. Emil and Fassl, Anna and Eibensteiner, Fabian and V{\"o}lkl-Kernstock, Sabine and Kletecka-Pulker, Maria and Crutzen, Rik and Gutenberg, Johanna and H{\"o}ppchen, Isabel and Niebauer, Josef and Smeddinck, David Jan and Willschke, Harald and Atanasov, G. Atanas", title="Research on Digital Technology Use in Cardiology: Bibliometric Analysis", journal="J Med Internet Res", year="2022", month="May", day="11", volume="24", number="5", pages="e36086", keywords="cardiovascular", keywords="heart", keywords="hypertension", keywords="atrial fibrillation", keywords="cardiopulmonary resuscitation", keywords="electrocardiography", keywords="photoplethysmography", keywords="wearable device, digital health, mHealth", keywords="cardiology", keywords="cardiac", keywords="health application", abstract="Background: Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area. Objective: We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field. Methods: The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization. Results: The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7\%), followed by United Kingdom (1136/12,529, 9.1\%), Germany (1067/12,529, 8.5\%), China (682/12,529, 5.4\%), and Italy (622/12,529, 5.0\%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2\%), atrial fibrillation (122/12,529, 1.0\%), atherosclerosis (116/12,529, 0.9\%), heart failure (106/12,529, 0.8\%), and arterial stiffness (80/12,529, 0.6\%). Recurring modalities were electrocardiography (170/12,529, 1.4\%), angiography (127/12,529, 1.0\%), echocardiography (127/12,529, 1.0\%), digital subtraction angiography (111/12,529, 0.9\%), and photoplethysmography (80/12,529, 0.6\%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2\%) and other JMIR portfolio journals (51/632, 8.0\%) were the major publishing venues. Conclusions: Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth. ", doi="10.2196/36086", url="https://www.jmir.org/2022/5/e36086", url="http://www.ncbi.nlm.nih.gov/pubmed/35544307" } @Article{info:doi/10.2196/34575, author="Yan, Hui and Rahgozar, Arya and Sethuram, Claire and Karunananthan, Sathya and Archibald, Douglas and Bradley, Lindsay and Hakimjavadi, Ramtin and Helmer-Smith, Mary and Jolin-Dahel, Kheira and McCutcheon, Tess and Puncher, Jeffrey and Rezaiefar, Parisa and Shoppoff, Lina and Liddy, Clare", title="Natural Language Processing to Identify Digital Learning Tools in Postgraduate Family Medicine: Protocol for a Scoping Review", journal="JMIR Res Protoc", year="2022", month="May", day="2", volume="11", number="5", pages="e34575", keywords="digital learning tools", keywords="medical education", keywords="primary care", keywords="digital learning", keywords="scoping review", keywords="family medicine", keywords="bibliometric", keywords="scientometric", keywords="natural language processing", keywords="e-learning", keywords="medical curriculum", keywords="medical curricula", keywords="medical school", abstract="Background: The COVID-19 pandemic has highlighted the growing need for digital learning tools in postgraduate family medicine training. Family medicine departments must understand and recognize the use and effectiveness of digital tools in order to integrate them into curricula and develop effective learning tools that fill gaps and meet the learning needs of trainees. Objective: This scoping review will aim to explore and organize the breadth of knowledge regarding digital learning tools in family medicine training. Methods: This scoping review follows the 6 stages of the methodological framework outlined first by Arksey and O'Malley, then refined by Levac et al, including a search of published academic literature in 6 databases (MEDLINE, ERIC, Education Source, Embase, Scopus, and Web of Science) and gray literature. Following title and abstract and full text screening, characteristics and main findings of the included studies and resources will be tabulated and summarized. Thematic analysis and natural language processing (NLP) will be conducted in parallel using a 9-step approach to identify common themes and synthesize the literature. Additionally, NLP will be employed for bibliometric and scientometric analysis of the identified literature. Results: The search strategy has been developed and launched. As of October 2021, we have completed stages 1, 2, and 3 of the scoping review. We identified 132 studies for inclusion through the academic literature search and 127 relevant studies in the gray literature search. Further refinement of the eligibility criteria and data extraction has been ongoing since September 2021. Conclusions: In this scoping review, we will identify and consolidate information and evidence related to the use and effectiveness of existing digital learning tools in postgraduate family medicine training. Our findings will improve the understanding of the current landscape of digital learning tools, which will be of great value to educators and trainees interested in using existing tools, innovators looking to design digital learning tools that meet current needs, and researchers involved in the study of digital tools. Trial Registration: OSF Registries osf.io/wju4k; https://osf.io/wju4k International Registered Report Identifier (IRRID): DERR1-10.2196/34575 ", doi="10.2196/34575", url="https://www.researchprotocols.org/2022/5/e34575", url="http://www.ncbi.nlm.nih.gov/pubmed/35499861" } @Article{info:doi/10.2196/28114, author="Nam, Seojin and Kim, Donghun and Jung, Woojin and Zhu, Yongjun", title="Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis", journal="J Med Internet Res", year="2022", month="Apr", day="22", volume="24", number="4", pages="e28114", keywords="deep learning", keywords="scientometric analysis", keywords="research publications", keywords="research landscape", keywords="research collaboration", keywords="knowledge diffusion", abstract="Background: Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird's-eye view of them. This absence has led to a partial and fragmented understanding of the field and its progress. Objective: This study aimed to gain a quantitative and qualitative understanding of the scientific domain by analyzing diverse bibliographic entities that represent the research landscape from multiple perspectives and levels of granularity. Methods: We searched and retrieved 978 deep learning studies in biomedicine from the PubMed database. A scientometric analysis was performed by analyzing the metadata, content of influential works, and cited references. Results: In the process, we identified the current leading fields, major research topics and techniques, knowledge diffusion, and research collaboration. There was a predominant focus on applying deep learning, especially convolutional neural networks, to radiology and medical imaging, whereas a few studies focused on protein or genome analysis. Radiology and medical imaging also appeared to be the most significant knowledge sources and an important field in knowledge diffusion, followed by computer science and electrical engineering. A coauthorship analysis revealed various collaborations among engineering-oriented and biomedicine-oriented clusters of disciplines. Conclusions: This study investigated the landscape of deep learning research in biomedicine and confirmed its interdisciplinary nature. Although it has been successful, we believe that there is a need for diverse applications in certain areas to further boost the contributions of deep learning in addressing biomedical research problems. We expect the results of this study to help researchers and communities better align their present and future work. ", doi="10.2196/28114", url="https://www.jmir.org/2022/4/e28114", url="http://www.ncbi.nlm.nih.gov/pubmed/35451980" } @Article{info:doi/10.2196/30258, author="Douze, Laura and Pelayo, Sylvia and Messaadi, Nassir and Grosjean, Julien and Kerdelhu{\'e}, Ga{\'e}tan and Marcilly, Romaric", title="Designing Formulae for Ranking Search Results: Mixed Methods Evaluation Study", journal="JMIR Hum Factors", year="2022", month="Mar", day="25", volume="9", number="1", pages="e30258", keywords="information retrieval", keywords="search engine", keywords="topical relevance", keywords="search result ranking", keywords="user testing", keywords="human factors", abstract="Background: A major factor in the success of any search engine is the relevance of the search results; a tool should sort the search results to present the most relevant documents first. Assessing the performance of the ranking formula is an important part of search engine evaluation. However, the methods currently used to evaluate ranking formulae mainly collect quantitative data and do not gather qualitative data, which help to understand what needs to be improved to tailor the formulae to their end users. Objective: This study aims to evaluate 2 different parameter settings of the ranking formula of LiSSa (the French acronym for scientific literature in health care; Department of Medical Informatics and Information), a tool that provides access to health scientific literature in French, to adapt the formula to the needs of the end users. Methods: To collect quantitative and qualitative data, user tests were carried out with representative end users of LiSSa: 10 general practitioners and 10 registrars. Participants first assessed the relevance of the search results and then rated the ranking criteria used in the 2 formulae. Verbalizations were analyzed to characterize each criterion. Results: A formula that prioritized articles representing a consensus in the field was preferred. When users assess an article's relevance, they judge its topic, methods, and value in clinical practice. Conclusions: Following the evaluation, several improvements were implemented to give more weight to articles that match the search topic and to downgrade articles that have less informative or scientific value for the reader. Applying a qualitative methodology generates valuable user inputs to improve the ranking formula and move toward a highly usable search engine. ", doi="10.2196/30258", url="https://humanfactors.jmir.org/2022/1/e30258", url="http://www.ncbi.nlm.nih.gov/pubmed/35333180" } @Article{info:doi/10.2196/33996, author="Nowlin, Ross and Wirtz, Alexis and Wenger, David and Ottwell, Ryan and Cook, Courtney and Arthur, Wade and Sallee, Brigitte and Levin, Jarad and Hartwell, Micah and Wright, Drew and Sealey, Meghan and Zhu, Lan and Vassar, Matt", title="Spin in Abstracts of Systematic Reviews and Meta-analyses of Melanoma Therapies: Cross-sectional Analysis", journal="JMIR Dermatol", year="2022", month="Feb", day="24", volume="5", number="1", pages="e33996", keywords="melanoma", keywords="spin", keywords="melanoma treatment", keywords="skin conditions", keywords="skin", keywords="misinterpreting data", keywords="misinterpretation", keywords="skin cancer", abstract="Background: Spin is defined as the misrepresentation of a study's results, which may lead to misperceptions or misinterpretation of the findings. Spin has previously been found in randomized controlled trials and systematic reviews of acne vulgaris treatments and treatments of various nondermatological conditions. Objective: The purpose of this study was to quantify the presence of spin in abstracts of systematic reviews and meta-analyses of melanoma therapies and identify any related secondary characteristics of these articles. Methods: We used a cross-sectional approach on June 2, 2020, to search the MEDLINE and Embase databases from their inception. To meet inclusion criteria, a study was required to be a systematic review or meta-analysis pertaining to the treatment of melanoma in human subjects, and reported in English. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) definition of systematic reviews and meta-analyses. Data were extracted in a masked, duplicate fashion. We conducted a powered bivariate linear regression and calculated odds ratios for each study characteristic. Results: A total of 200 systematic reviews met the inclusion criteria. We identified spin in 38\% (n=76) of the abstracts. The most common type of spin found was type 3 (selective reporting of or overemphasis on efficacy outcomes or analysis favoring the beneficial effect of the experimental intervention), occurring 40 times; the least common was type 2 (title claims or suggests a beneficial effect of the experimental intervention not supported by the findings), which was not present in any included abstracts. We found that abstracts pertaining to pharmacologic interventions were 3.84 times more likely to contain spin. The likelihood of an article containing spin has decreased annually (adjusted odds ratio 0.91, 95\% CI 0.84-0.99). No significant correlation between funding source or other study characteristics and the presence of spin was identified. Conclusions: We have found that spin is fairly common in the abstracts of systematic reviews of melanoma treatments, but the prevalence of spin in these abstracts has been declining from 1992-2020. ", doi="10.2196/33996", url="https://derma.jmir.org/2022/1/e33996", url="http://www.ncbi.nlm.nih.gov/pubmed/37632865" } @Article{info:doi/10.2196/25238, author="Zheng, Fuhao and Wang, Ling and Zeng, Zhaonan and Wu, Siying", title="International Technologies on Prevention and Treatment of Neurological and Psychiatric Diseases: Bibliometric Analysis of Patents", journal="JMIR Ment Health", year="2022", month="Feb", day="22", volume="9", number="2", pages="e25238", keywords="neurological diseases", keywords="psychiatric diseases", keywords="patent", keywords="bibliometric analysis", keywords="prevention", keywords="treatment", abstract="Background: Neurological and psychiatric disorders are serious and expensive global public health problems. Therefore, exploring effective intervention technologies plays an important role in improving patients' clinical symptoms and social functions, as well as reducing medical burden. Objective: The aim of this study is to analyze and summarize the key new technologies and innovative development trends witnessed globally for neurological illness and psychiatric disorders by mining the relevant patent data. Methods: A bibliometric analysis was conducted on patent applications, priority countries, main patentees, hot technologies, and other patent information on neurological and psychiatric disorders, revealing the current situation along with the trend of technology development in this field. Results: In recent years, inventions and innovations related to neurological and psychiatric diseases have become very active, with China being the largest patent priority country. Of the top patent holders, Visicu (headquartered in the United States) is the leader. The distribution of patent holders in China remains relatively scattered, with no monopoly organization at present. Global technologies on neurological illness and psychiatric disorders are mainly concentrated around A61B (diagnosis, surgery, and identification). Conclusions: This paper analyzed and summarized the key new technologies and global innovative development trends of neurological and psychiatric diseases by mining the relevant patent data, and provides practical references and research perspectives for the prevention and treatment of the aforesaid diseases. ", doi="10.2196/25238", url="https://mental.jmir.org/2022/2/e25238", url="http://www.ncbi.nlm.nih.gov/pubmed/35191849" } @Article{info:doi/10.2196/32747, author="Yeung, Kan Andy Wai and Parvanov, D. Emil and Hribersek, Mojca and Eibensteiner, Fabian and Klager, Elisabeth and Kletecka-Pulker, Maria and R{\"o}ssler, Bernhard and Schebesta, Karl and Willschke, Harald and Atanasov, G. Atanas and Schaden, Eva", title="Digital Teaching in Medical Education: Scientific Literature Landscape Review", journal="JMIR Med Educ", year="2022", month="Feb", day="9", volume="8", number="1", pages="e32747", keywords="medical education", keywords="digital teaching", keywords="virtual reality", keywords="augmented reality", keywords="anatomy", keywords="basic life support", keywords="satisfaction", keywords="bibliometric", keywords="medicine", keywords="life support", keywords="online learning", keywords="literature", keywords="trend", keywords="citation", abstract="Background: Digital teaching in medical education has grown in popularity in the recent years. However, to the best of our knowledge, no bibliometric report to date has been published that analyzes this important literature set to reveal prevailing topics and trends and their impacts reflected in citation counts. Objective: We used a bibliometric approach to unveil and evaluate the scientific literature on digital teaching research in medical education, demonstrating recurring research topics, productive authors, research organizations, countries, and journals. We further aimed to discuss some of the topics and findings reported by specific highly cited works. Methods: The Web of Science electronic database was searched to identify relevant papers on digital teaching research in medical education. Basic bibliographic data were obtained by the ``Analyze'' and ``Create Citation Report'' functions of the database. Complete bibliographic data were exported to VOSviewer for further analyses. Visualization maps were generated to display the recurring author keywords and terms mentioned in the titles and abstracts of the publications. Results: The analysis was based on data from 3978 papers that were identified. The literature received worldwide contributions with the most productive countries being the United States and United Kingdom. Reviews were significantly more cited, but the citations between open access vs non--open access papers did not significantly differ. Some themes were cited more often, reflected by terms such as virtual reality, innovation, trial, effectiveness, and anatomy. Different aspects in medical education were experimented for digital teaching, such as gross anatomy education, histology, complementary medicine, medicinal chemistry, and basic life support. Some studies have shown that digital teaching could increase learning satisfaction, knowledge gain, and even cost-effectiveness. More studies were conducted on trainees than on undergraduate students. Conclusions: Digital teaching in medical education is expected to flourish in the future, especially during this era of COVID-19 pandemic. ", doi="10.2196/32747", url="https://mededu.jmir.org/2022/1/e32747", url="http://www.ncbi.nlm.nih.gov/pubmed/35138260" } @Article{info:doi/10.2196/34051, author="Kudlow, Paul and Brown, Tashauna and Eysenbach, Gunther", title="Citation Advantage of Promoted Articles in a Cross-Publisher Distribution Platform: 36-Month Follow-up to a Randomized Controlled Trial", journal="J Med Internet Res", year="2021", month="Dec", day="10", volume="23", number="12", pages="e34051", keywords="knowledge translation", keywords="knowledge", keywords="dissemination", keywords="digital knowledge translation", keywords="digital publishing", keywords="e-publishing", keywords="open access", keywords="scientometrics", keywords="infometrics", abstract="Background: There are limited evidence-based strategies that have been shown to increase the rate at which peer-reviewed articles are cited. In a previously reported randomized controlled trial, we demonstrated that promotion of article links in an online cross-publisher distribution platform (TrendMD) persistently augments citation rates after 12 months, leading to a statistically significant 50\% increase in citations relative to the control. Objective: This study aims to investigate if the citation advantage of promoted articles upholds after 36 months. Methods: A total of 3200 published articles in 64 peer-reviewed journals across 8 subject areas were block randomized at the subject level to either the TrendMD group (n=1600) or the control group (n=1600) of the study. Articles were promoted in the TrendMD Network for 6 months. We compared the citation rates in both groups after 36 months. Results: At 36 months, we found the citation advantage endured; articles randomized to TrendMD showed a 28\% increase in mean citations relative to the control. The difference in mean citations at 36 months for articles randomized to TrendMD versus the control was 10.52 (95\% CI 3.79-17.25) and was statistically significant (P=.001). Conclusions: To our knowledge, this is the first randomized controlled trial to demonstrate how a postpublication article promotion intervention can be used to persistently augment citations of peer-reviewed articles. TrendMD is an efficient digital tool for knowledge translation and dissemination to targeted audiences to facilitate the uptake of research. ", doi="10.2196/34051", url="https://www.jmir.org/2021/12/e34051", url="http://www.ncbi.nlm.nih.gov/pubmed/34890350" } @Article{info:doi/10.2196/32948, author="Ellis, A. Louise and Meulenbroeks, Isabelle and Churruca, Kate and Pomare, Chiara and Hatem, Sarah and Harrison, Reema and Zurynski, Yvonne and Braithwaite, Jeffrey", title="The Application of e-Mental Health in Response to COVID-19: Scoping Review and Bibliometric Analysis", journal="JMIR Ment Health", year="2021", month="Dec", day="6", volume="8", number="12", pages="e32948", keywords="e-mental health", keywords="mental health", keywords="COVID-19", keywords="bibliometrics", keywords="health systems", abstract="Background: The COVID-19 pandemic and its mitigation measures and impacts, such as shelter-in-place orders, social isolation, restrictions on freedoms, unemployment, financial insecurity, and disrupted routines, have led to declines in mental health worldwide and concomitant escalating demands for mental health services. Under the circumstances, electronic mental health (e-mental health) programs and services have rapidly become the ``new normal.'' Objective: The aim of this study was to assess key characteristics and evidence gaps in the e-mental health literature published in relation to the COVID-19 pandemic via a scoping review and bibliometric analysis. Methods: We conducted a search of four academic databases (ie, MEDLINE, Embase, PsycInfo, and CINAHL) for documents published from December 31, 2019, to March 31, 2021, using keywords for e-mental health and COVID-19. Article information was extracted that was relevant to the review objective, including journal, type of article, keywords, focus, and corresponding author. Information was synthesized by coding these attributes and was then summarized through descriptive statistics and narrative techniques. Article influence was examined from Altmetric and CiteScore data, and a network analysis was conducted on article keywords. Results: A total of 356 publications were included in the review. Articles on e-mental health quickly thrived early in the pandemic, with most articles being nonempirical, chiefly commentaries or opinions (n=225, 63.2\%). Empirical publications emerged later and became more frequent as the pandemic progressed. The United States contributed the most articles (n=160, 44.9\%), though a notable number came from middle-income countries (n=59, 16.6\%). Articles were spread across 165 journals and had above-average influence (ie, almost half of the articles were in the top 25\% of output scores by Altmetric, and the average CiteScore across articles was 4.22). The network analysis of author-supplied keywords identified key topic areas, including specific mental disorders, eHealth modalities, issues and challenges, and populations of interest. These were further explored via full-text analysis. Applications of e-mental health during the pandemic overcame, or were influenced by, system, service, technology, provider, and patient factors. Conclusions: COVID-19 has accelerated applications of e-mental health. Further research is needed to support the implementation of e-mental health across system and service infrastructures, alongside evidence of the relative effectiveness of e-mental health in comparison to traditional modes of care. ", doi="10.2196/32948", url="https://mental.jmir.org/2021/12/e32948", url="http://www.ncbi.nlm.nih.gov/pubmed/34666306" } @Article{info:doi/10.2196/32721, author="Pawassar, Matthias Christian and Tiberius, Victor", title="Virtual Reality in Health Care: Bibliometric Analysis", journal="JMIR Serious Games", year="2021", month="Dec", day="1", volume="9", number="4", pages="e32721", keywords="virtual reality", keywords="healthcare", keywords="bibliometric analysis", keywords="literature review", keywords="citation analysis", keywords="VR", keywords="usability", keywords="review", keywords="health care", abstract="Background: Research into the application of virtual reality technology in the health care sector has rapidly increased, resulting in a large body of research that is difficult to keep up with. Objective: We will provide an overview of the annual publication numbers in this field and the most productive and influential countries, journals, and authors, as well as the most used, most co-occurring, and most recent keywords. Methods: Based on a data set of 356 publications and 20,363 citations derived from Web of Science, we conducted a bibliometric analysis using BibExcel, HistCite, and VOSviewer. Results: The strongest growth in publications occurred in 2020, accounting for 29.49\% of all publications so far. The most productive countries are the United States, the United Kingdom, and Spain; the most influential countries are the United States, Canada, and the United Kingdom. The most productive journals are the Journal of Medical Internet Research (JMIR), JMIR Serious Games, and the Games for Health Journal; the most influential journals are Patient Education and Counselling, Medical Education, and Quality of Life Research. The most productive authors are Riva, del Piccolo, and Schwebel; the most influential authors are Finset, del Piccolo, and Eide. The most frequently occurring keywords other than ``virtual'' and ``reality'' are ``training,'' ``trial,'' and ``patients.'' The most relevant research themes are communication, education, and novel treatments; the most recent research trends are fitness and exergames. Conclusions: The analysis shows that the field has left its infant state and its specialization is advancing, with a clear focus on patient usability. ", doi="10.2196/32721", url="https://games.jmir.org/2021/4/e32721", url="http://www.ncbi.nlm.nih.gov/pubmed/34855606" } @Article{info:doi/10.2196/31510, author="Warin, Thierry", title="Global Research on Coronaviruses: Metadata-Based Analysis for Public Health Policies", journal="JMIR Med Inform", year="2021", month="Nov", day="30", volume="9", number="11", pages="e31510", keywords="COVID-19", keywords="SARS-CoV-2", keywords="natural language processing", keywords="coronavirus", keywords="unstructured data", keywords="data science", keywords="health 4.0", abstract="Background: Within the context of the COVID-19 pandemic, this paper suggests a data science strategy for analyzing global research on coronaviruses. The application of reproducible research principles founded on text-as-data information, open science, the dissemination of scientific data, and easy access to scientific production may aid public health in the fight against the virus. Objective: The primary goal of this paper was to use global research on coronaviruses to identify critical elements that can help inform public health policy decisions. We present a data science framework to assist policy makers in implementing cutting-edge data science techniques for the purpose of developing evidence-based public health policies. Methods: We used the EpiBibR (epidemiology-based bibliography for R) package to gain access to coronavirus research documents worldwide (N=121,231) and their associated metadata. To analyze these data, we first employed a theoretical framework to group the findings into three categories: conceptual, intellectual, and social. Second, we mapped the results of our analysis in these three dimensions using machine learning techniques (ie, natural language processing) and social network analysis. Results: Our findings, firstly, were methodological in nature. They demonstrated the potential for the proposed data science framework to be applied to public health policies. Additionally, our findings indicated that the United States and China were the primary contributors to global coronavirus research during the study period. They also demonstrated that India and Europe were significant contributors, albeit in a secondary position. University collaborations in this domain were strong between the United States, Canada, and the United Kingdom, confirming the country-level findings. Conclusions: Our findings argue for a data-driven approach to public health policy, particularly when efficient and relevant research is required. Text mining techniques can assist policy makers in calculating evidence-based indices and informing their decision-making process regarding specific actions necessary for effective health responses. ", doi="10.2196/31510", url="https://medinform.jmir.org/2021/11/e31510", url="http://www.ncbi.nlm.nih.gov/pubmed/34596570" } @Article{info:doi/10.2196/25394, author="Santisteban-Espejo, Antonio and Martin-Piedra, Angel Miguel and Campos, Antonio and Moran-Sanchez, Julia and Cobo, J. Manuel and Pacheco-Serrano, I. Ana and Moral-Munoz, A. Jose", title="Information and Scientific Impact of Advanced Therapies in the Age of Mass Media: Altmetrics-Based Analysis of Tissue Engineering", journal="J Med Internet Res", year="2021", month="Nov", day="26", volume="23", number="11", pages="e25394", keywords="advanced therapies", keywords="tissue engineering", keywords="scientometrics", keywords="altmetrics", keywords="online", keywords="web", keywords="communication of science", abstract="Background: Tissue engineering (TE) constitutes a multidisciplinary field aiming to construct artificial tissues to regenerate end-stage organs. Its development has taken place since the last decade of the 20th century, entailing a clinical revolution. TE research groups have worked and shared relevant information in the mass media era. Thus, it would be interesting to study the online dimension of TE research and to compare it with traditional measures of scientific impact. Objective: The objective of this study was to evaluate the online dimension of TE documents from 2012 to 2018 using metadata obtained from the Web of Science (WoS) and Altmetric and to develop a prediction equation for the impact of TE documents from altmetric scores. Methods: We analyzed 10,112 TE documents through descriptive and statistical methods. First, the TE temporal evolution was exposed for WoS and 15 online platforms (news, blogs, policy, Twitter, patents, peer review, Weibo, Facebook, Wikipedia, Google, Reddit, F1000, Q\&A, video, and Mendeley Readers). The 10 most cited TE original articles were ranked according to the normalized WoS citations and the normalized Altmetric Attention Score. Second, to better comprehend the TE online framework, correlation and factor analyses were performed based on the suitable results previously obtained for the Bartlett sphericity and Kaiser--Meyer--Olkin tests. Finally, the linear regression model was applied to elucidate the relation between academics and online media and to construct a prediction equation for TE from altmetrics data. Results: TE dynamic shows an upward trend in WoS citations, Twitter, Mendeley Readers, and Altmetric Scores. However, WoS and Altmetric rankings for the most cited documents clearly differ. When compared, the best correlation results were obtained for Mendeley Readers and WoS ($\rho$=0.71). In addition, the factor analysis identified 6 factors that could explain the previously observed differences between academic institutions and the online platforms evaluated. At this point, the mathematical model constructed is able to predict and explain more than 40\% of TE WoS citations from Altmetric scores. Conclusions: Scientific information related to the construction of bioartificial tissues increasingly reaches society through different online media. Because the focus of TE research importantly differs when the academic institutions and online platforms are compared, basic and clinical research groups, academic institutions, and health politicians should make a coordinated effort toward the design and implementation of adequate strategies for information diffusion and population health education. ", doi="10.2196/25394", url="https://www.jmir.org/2021/11/e25394", url="http://www.ncbi.nlm.nih.gov/pubmed/34842548" } @Article{info:doi/10.2196/31142, author="Gong, Jianxia and Sihag, Vikrant and Kong, Qingxia and Zhao, Lindu", title="Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis", journal="JMIR Med Inform", year="2021", month="Nov", day="1", volume="9", number="11", pages="e31142", keywords="knowledge evolution trends", keywords="research hotspots", keywords="personal health data", keywords="bibliometrics", abstract="Background: The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. Objective: The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. Methods: We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. Results: The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. Conclusions: Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including ``allocative value,'' ``technology value,'' and ``personalized value.'' ", doi="10.2196/31142", url="https://medinform.jmir.org/2021/11/e31142", url="http://www.ncbi.nlm.nih.gov/pubmed/34723823" } @Article{info:doi/10.2196/32639, author="Saad, K. Randa and Al Nsour, Mohannad and Khader, Yousef and Al Gunaid, Magid", title="Public Health Surveillance Systems in the Eastern Mediterranean Region: Bibliometric Analysis of Scientific Literature", journal="JMIR Public Health Surveill", year="2021", month="Nov", day="1", volume="7", number="11", pages="e32639", keywords="public health", keywords="surveillance", keywords="Eastern Mediterranean Region", keywords="bibliometric analysis", keywords="literature", keywords="research", keywords="review", abstract="Background: The Eastern Mediterranean Region (EMR) hosts some of the world's worst humanitarian and health crises. The implementation of health surveillance in this region has faced multiple constraints. New and novel approaches in surveillance are in a constant state of high and immediate demand. Identifying the existing literature on surveillance helps foster an understanding of scientific development and thus potentially supports future development directions. Objective: This study aims to illustrate the scientific production, quantify the scholarly impact, and highlight the characteristics of publications on public health surveillance in the EMR over the past decade. Methods: We performed a Scopus search using keywords related to public health surveillance or its disciplines, cross-referenced with EMR countries, from 2011 to July 2021. Data were exported and analyzed using Microsoft Excel and Visualization of Similarities Viewer. Quality of journals was determined using SCImago Journal Rank and CiteScore. Results: We retrieved 1987 documents, of which 1927 (96.98\%) were articles or reviews. There has been an incremental increase in the number of publications (exponential growth, R2=0.80) over the past decade. Publications were mostly affiliated with Iran (501/1987, 25.21\%), the United States (468/1987, 23.55\%), Pakistan (243/1987, 12.23\%), Egypt (224/1987, 11.27\%), and Saudi Arabia (209/1987, 10.52\%). However, Iran only had links with 40 other countries (total link strength 164), and the biggest collaborator from the EMR was Egypt, with 67 links (total link strength 402). Within the other EMR countries, only Morocco, Lebanon, and Jordan produced ?79 publications in the 10-year period. Most publications (1551/1987, 78.06\%) were affiliated with EMR universities. Most journals were categorized as medical journals, and the highest number of articles were published in the Eastern Mediterranean Health Journal (SCImago Journal Rank 0.442; CiteScore 1.5). Retrieved documents had an average of 18.4 (SD 125.5) citations per document and an h-index of 66. The top-3 most cited documents were from the Global Burden of Diseases study. We found 70 high-frequency terms, occurring ?10 times in author keywords, connected in 3 clusters. COVID-19, SARS-CoV-2, and pandemic represented the most recent 2020 cluster. Conclusions: This is the first research study to quantify the published literature on public health surveillance and its disciplines in the EMR. Research productivity has steadily increased over the past decade, and Iran has been the leading country publishing relevant research. Recurrent recent surveillance themes included COVID-19 and SARS-CoV-2. This study also sheds light on the gaps in surveillance research in the EMR, including inadequate publications on noncommunicable diseases and injury-related surveillance. ", doi="10.2196/32639", url="https://publichealth.jmir.org/2021/11/e32639", url="http://www.ncbi.nlm.nih.gov/pubmed/34723831" } @Article{info:doi/10.2196/26691, author="Sadatmoosavi, Ali and Tajedini, Oranus and Esmaeili, Omid and Abolhasani Zadeh, Firouzeh and Khazaneha, Mahdiyeh", title="Emerging Trends and Thematic Evolution of Breast Cancer: Knowledge Mapping and Co-Word Analysis", journal="JMIR Cancer", year="2021", month="Oct", day="28", volume="7", number="4", pages="e26691", keywords="scientometrics", keywords="breast cancer", keywords="co-word analysis", keywords="Scimat", keywords="science mapping", abstract="Background: One of the requirements for scientists and researchers to enter any field of science is to have a comprehensive and accurate understanding of that discipline. Objective: This study aims to draw a science map, provide structural analysis, explore the evolution, and determine new trends in research articles published in the field of breast cancer. Methods: This study comprised a descriptive survey with a scientometric approach. Data were collected from MEDLINE using a search strategy based on Medical Subject Heading (MeSH) terms. This study used science mapping, which provides a visual representation and a longitudinal evolution of possible interrelations between scientific areas, documents, or authors, thus reflecting the cognitive architecture of science mapping. For this scientometric evaluation of the topic of breast cancer research, a very long period was considered for data collection. Moreover, due to the availability of numerous publications in the database, the assessment was divided into three different periods ranging from 1988 to 2020. Results: A total of 12,577 records related to scientometric studies were extracted. The field of breast cancer research demonstrated three diagrams containing the most relevant themes for the three chronological periods evaluated. Each diagram was plotted based on the centrality and density linked to each research topic. The research output in the field was observed to revolve around 8 areas or themes: radiation injury, cardiovascular disease, fibroadenoma, antineoplastic agent, estrogen antagonistic, immunohistochemistry, soybean, and epitopes, each represented with different colors. Conclusions: In the strategic diagrams, the themes were both well developed and important for the structuring of a research field. The first quadrant comprised motor themes of the specialty, which present strong centrality and high density (eg, corticosteroid antineoplastic age, stem cell, T-lymphocyte, protein tyrosine kinase, dietary, and phosphatidyl inositol-3-kinase). In the second quadrant of diagram, themes have well-developed internal ties but unimportant external ties, as they are of only marginal importance for the field. These themes are very specialized and peripheral (eg, DNA-binding). In the third quadrant, themes are both weakly developed and marginal. The themes in this quadrant have low density and centrality and mainly represent either emerging or declining themes (eg, ovarian neoplasm). Themes in the fourth quadrant of the strategic diagram are considered important for a research field but are not fully developed. This quadrant contains transversal and general, basic themes (eg, immunohistochemistry). Scientometric analysis of breast cancer research can be regarded as a roadmap for future research and policymaking for this important field. ", doi="10.2196/26691", url="https://cancer.jmir.org/2021/4/e26691", url="http://www.ncbi.nlm.nih.gov/pubmed/34709188" } @Article{info:doi/10.2196/29809, author="Clavier, Thomas and Occhiali, Emilie and Demailly, Zo{\'e} and Comp{\`e}re, Vincent and Veber, Benoit and Selim, Jean and Besnier, Emmanuel", title="The Association Between Professional Accounts on Social Networks Twitter and ResearchGate and the Number of Scientific Publications and Citations Among Anesthesia Researchers: Observational Study", journal="J Med Internet Res", year="2021", month="Oct", day="15", volume="23", number="10", pages="e29809", keywords="social network", keywords="anesthesia", keywords="publication", keywords="Twitter", keywords="ResearchGate", keywords="citation", keywords="social media", keywords="academic", keywords="researcher", keywords="bibliometrics", keywords="research output", abstract="Background: Social networks are now essential tools for promoting research and researchers. However, there is no study investigating the link between presence or not on professional social networks and scientific publication or citation for a given researcher. Objective: The objective of this study was to study the link between professional presence on social networks and scientific publications/citations among anesthesia researchers. Methods: We included all the French full professors and associate professors of anesthesia. We analyzed their presence on the social networks Twitter (professional account with ?1 tweet over the 6 previous months) and ResearchGate. We extracted their bibliometric parameters for the 2016-2020 period via the Web of Science Core Collection (Clarivate Analytics) database in the Science Citation Index-Expanded index. Results: A total of 162 researchers were analyzed; 42 (25.9\%) had an active Twitter account and 110 (67.9\%) a ResearchGate account. There was no difference between associate professors and full professors regarding active presence on Twitter (8/23 [35\%] vs. 34/139 [24.5\%], respectively; P=.31) or ResearchGate (15/23 [65\%] vs. 95/139 [68.3\%], respectively; P=.81). Researchers with an active Twitter account (median [IQR]) had more scientific publications (45 [28-61] vs. 26 [12-41]; P<.001), a higher h-index (12 [8-16] vs. 8 [5-11]; P<.001), a higher number of citations per publication (12.54 [9.65-21.8] vs. 10.63 [5.67-16.10]; P=.01), and a higher number of citations (563 [321-896] vs. 263 [105-484]; P<.001). Researchers with a ResearchGate account (median [IQR]) had more scientific publications (33 [17-47] vs. 26 [9-43]; P=.03) and a higher h-index (9 [6-13] vs. 8 [3-11]; P=.03). There was no difference between researchers with a ResearchGate account and those without it concerning the number of citations per publication and overall number of citations. In multivariate analysis including sex, academic status, and presence on social networks, the presence on Twitter was associated with the number of publications ($\beta$=20.2; P<.001), the number of citations ($\beta$=494.5; P<.001), and the h-index ($\beta$=4.5; P<.001). Conclusions: Among French anesthesia researchers, an active presence on Twitter is associated with higher scientific publication and citations. ", doi="10.2196/29809", url="https://www.jmir.org/2021/10/e29809", url="http://www.ncbi.nlm.nih.gov/pubmed/34652279" } @Article{info:doi/10.2196/29239, author="Qua, Kelli and Yu, Fei and Patel, Tanha and Dave, Gaurav and Cornelius, Katherine and Pelfrey, M. Clara", title="Scholarly Productivity Evaluation of KL2 Scholars Using Bibliometrics and Federal Follow-on Funding: Cross-Institution Study", journal="J Med Internet Res", year="2021", month="Sep", day="29", volume="23", number="9", pages="e29239", keywords="bibliometrics", keywords="Clinical and Translational Science Award", keywords="KL2", keywords="translational research", keywords="career development", abstract="Background: Evaluating outcomes of the clinical and translational research (CTR) training of a Clinical and Translational Science Award (CTSA) hub (eg, the KL2 program) requires the selection of reliable, accessible, and standardized measures. As measures of scholarly success usually focus on publication output and extramural funding, CTSA hubs have started to use bibliometrics to evaluate the impact of their supported scholarly activities. However, the evaluation of KL2 programs across CTSAs is limited, and the use of bibliometrics and follow-on funding is minimal. Objective: This study seeks to evaluate scholarly productivity, impact, and collaboration using bibliometrics and federal follow-on funding of KL2 scholars from 3 CTSA hubs and to define and assess CTR training success indicators. Methods: The sample included KL2 scholars from 3 CTSA institutions (A-C). Bibliometric data for each scholar in the sample were collected from both SciVal and iCite, including scholarly productivity, citation impact, and research collaboration. Three federal follow-on funding measures (at the 5-year, 8-year, and overall time points) were collected internally and confirmed by examining a federal funding database. Both descriptive and inferential statistical analyses were computed using SPSS to assess the bibliometric and federal follow-on funding results. Results: A total of 143 KL2 scholars were included in the sample with relatively equal groups across the 3 CTSA institutions. The included KL2 scholars produced more publications and citation counts per year on average at the 8-year time point (3.75 publications and 26.44 citation counts) than the 5-year time point (3.4 publications vs 26.16 citation counts). Overall, the KL2 publications from all 3 institutions were cited twice as much as others in their fields based on the relative citation ratio. KL2 scholars published work with researchers from other US institutions over 2 times (5-year time point) or 3.5 times (8-year time point) more than others in their research fields. Within 5 years and 8 years postmatriculation, 44.1\% (63/143) and 51.7\% (74/143) of KL2 scholars achieved federal funding, respectively. The KL2-scholars of Institution C had a significantly higher citation rate per publication than the other institutions (P<.001). Institution A had a significantly lower rate of nationally field-weighted collaboration than did the other institutions (P<.001). Institution B scholars were more likely to have received federal funding than scholars at Institution A or C (P<.001). Conclusions: Multi-institutional data showed a high level of scholarly productivity, impact, collaboration, and federal follow-on funding achieved by KL2 scholars. This study provides insights on the use of bibliometric and federal follow-on funding data to evaluate CTR training success across institutions. CTSA KL2 programs and other CTR career training programs can benefit from these findings in terms of understanding metrics of career success and using that knowledge to develop highly targeted strategies to support early-stage career development of CTR investigators. ", doi="10.2196/29239", url="https://www.jmir.org/2021/9/e29239", url="http://www.ncbi.nlm.nih.gov/pubmed/34586077" } @Article{info:doi/10.2196/24831, author="Cao, Jiamin and Wang, Nuo and Hou, Shiying and Qi, Xin and Chen, Yu and Xiong, Wei", title="Overview of Graves Ophthalmopathy Literature From 1999 to 2019: Bibliometric Analysis", journal="Interact J Med Res", year="2021", month="Sep", day="28", volume="10", number="3", pages="e24831", keywords="Graves ophthalmopathy", keywords="bibliometric analysis", keywords="CiteSpace", keywords="Web of Science", abstract="Background: Research on Graves ophthalmopathy has increased remarkably over the last 2 decades; however, few statistical analyses of the data presented in these publications have been conducted. Objective: This study aims to detect and analyze emerging trends and collaboration networks in Graves ophthalmopathy research. Methods: Graves ophthalmopathy--related publications from 1999 to 2019 were collected from the Web of Science Core Collection Database. Collected publications were restricted by category (article or review) and language (English). Bibliometric analyses included changes in the annual numbers of publications, journals, authors, countries, institutions, keywords, and references. Results: In total, 3051 publications that met the criteria were collected. The number of annual publications has exhibited an increasing trend over the last 20 years. The journal Thyroid ranked first, publishing 183 Graves ophthalmopathy--related studies. There was no evidence of a relationship between impact factor (IF) and the number of publications (P=.69). The author Smith TJ had the largest number of publications on Graves ophthalmopathy (n=83). Of the countries that had published Graves ophthalmopathy--related articles, the United States had the largest number (n=784) and the highest centrality (0.18). Among institutions, the University of Pisa (Italy) contributed the most Graves ophthalmopathy--related articles (n=114). The most recent burst keywords (proliferation, rituximab, and selenium) and references may provide clues on emerging trends in research and clinical practice. Conclusions: This bibliometric analysis highlights countries, institutions, and authors who contributed to Graves ophthalmopathy--related publications. Emerging trends in Graves ophthalmopathy research, based on burst keywords and references, may provide clues relevant to clinical practice and future research. ", doi="10.2196/24831", url="https://www.i-jmr.org/2021/3/e24831", url="http://www.ncbi.nlm.nih.gov/pubmed/34581676" } @Article{info:doi/10.2196/29282, author="Pulsipher, J. Kayd and Szeto, D. Mindy and Rundle, W. Chandler and Presley, L. Colby and Laughter, R. Melissa and Dellavalle, P. Robert", title="Global Burden of Skin Disease Representation in the Literature: Bibliometric Analysis", journal="JMIR Dermatol", year="2021", month="Aug", day="31", volume="4", number="2", pages="e29282", keywords="global burden of disease", keywords="global health", keywords="global dermatology", keywords="disability-adjusted life years", keywords="GBD", keywords="DALYs", keywords="journalology", keywords="dermatology", keywords="skin disorders", abstract="Background: The global burden of skin disease may be reduced through research efforts focused on skin diseases with the highest reported disability-adjusted life years. Objective: This study evaluates the representation of dermatologic conditions comprising the highest disability-adjusted life years in dermatology literature to identify areas that could benefit from greater research focus. Methods: The top 10 skin disorders according to their respective disability-adjusted life years as per the 2013 Global Burden of Disease were identified using previous studies. The top 5 dermatology journals ranked by the 2019 h-index were also identified. A PubMed search of each journal was performed using individual skin disease terms. From 2015 to 2020, all indexed publications pertaining to each disease were recorded and compared to the total number of publications for each journal surveyed. Results: A total of 19,727 papers were published in the 5 journals over the span of 2015-2020. Although melanoma ranked as the eighth highest in disability-adjusted life years, it had the highest representation in the literature (1995/19,727, 10.11\%). Melanoma was followed in representation by psoriasis (1936/19,727, 9.81\%) and dermatitis (1927/19,727, 9.77\%). These 3 conditions comprised a total of 29.69\% (5858/19,727) of the total publications, while the remaining 7 skin conditions were represented by a combined 6.79\% (1341/19,727) of the total publications. Conclusions: This research identifies gaps in the literature related to the top skin diseases contributing to the global burden of disease. Our study provides insight into future opportunities of focused research on less-studied skin diseases to potentially aid in reducing the global burden of skin disease. ", doi="10.2196/29282", url="https://derma.jmir.org/2021/2/e29282", url="http://www.ncbi.nlm.nih.gov/pubmed/37632830" } @Article{info:doi/10.2196/26378, author="Erskine, Natalie and Hendricks, Sharief", title="The Use of Twitter by Medical Journals: Systematic Review of the Literature", journal="J Med Internet Res", year="2021", month="Jul", day="28", volume="23", number="7", pages="e26378", keywords="Twitter", keywords="social media", keywords="medical journals", keywords="impact", abstract="Background: Medical journals use Twitter to engage and disseminate their research articles and implement a range of strategies to maximize reach and impact. Objective: This study aims to systematically review the literature to synthesize and describe the different Twitter strategies used by medical journals and their effectiveness on journal impact and readership metrics. Methods: A systematic search of the literature before February 2020 in four electronic databases (PubMed, Web of Science, Scopus, and ScienceDirect) was conducted. Articles were reviewed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. Results: The search identified 44 original research studies that evaluated Twitter strategies implemented by medical journals and analyzed the relationship between Twitter metrics and alternative and citation-based metrics. The key findings suggest that promoting publications on Twitter improves citation-based and alternative metrics for academic medical journals. Moreover, implementing different Twitter strategies maximizes the amount of attention that publications and journals receive. The four key Twitter strategies implemented by many medical journals are tweeting the title and link of the article, infographics, podcasts, and hosting monthly internet-based journal clubs. Each strategy was successful in promoting the publications. However, different metrics were used to measure success. Conclusions: Four key Twitter strategies are implemented by medical journals: tweeting the title and link of the article, infographics, podcasts, and hosting monthly internet-based journal clubs. In this review, each strategy was successful in promoting publications but used different metrics to measure success. Thus, it is difficult to conclude which strategy is most effective. In addition, the four strategies have different costs and effects on dissemination and readership. We recommend that journals and researchers incorporate a combination of Twitter strategies to maximize research impact and capture audiences with a variety of learning methods. ", doi="10.2196/26378", url="https://www.jmir.org/2021/7/e26378", url="http://www.ncbi.nlm.nih.gov/pubmed/34319238" } @Article{info:doi/10.2196/25700, author="Ni, Bowen and He, Minyi and Cao, Bei and He, Jianmin and Liu, Yawei and Zhao, Zhen", title="Status Quo and Research Trends of Neurosurgical Departments in China: Bibliometric and Scientometric Analyses", journal="J Med Internet Res", year="2021", month="Jul", day="5", volume="23", number="7", pages="e25700", keywords="neurosurgery", keywords="bibliometric analysis", keywords="co-word biclustering analysis", keywords="visualized analysis", abstract="Background: Modern neurosurgery is a relatively young discipline characterized by finesse and complexity. In recent years, neurosurgery in China has made continuous developments, with long-term progress and outstanding discoveries in many aspects of the field. Objective: This scientometric investigation aimed to comprehensively provide insight into the development trends of neurosurgery in China, to demonstrate how the field has evolved. Methods: PubMed database was searched to retrieve relevant papers published between 1988 and 2018 from neurosurgery institutions in China. The database of the National Natural Science Foundation of China was also retrieved for funding information. Information (eg, year of publication, journal, institute of origin) and keywords were collected from each paper after removing duplicates and filtering unintentional words. Co-word analysis was performed on the papers' keywords, and a time distribution matrix of coexisting keywords in a given paper (ie, termed co-words) was established. Co-words were clustered according to their growth rate within years and visually presented with a mountain plot and a heatmap. Trends and potential subspecialties were identified, and each topic, represented either by a co-word from publications or funding from the National Natural Science Foundation of China during the period from 2011 to 2018, was collected and analyzed. Results: Within 15,972 publications on neurosurgery from institutions in China, diagnostic image was found to coexist the most with other keywords. Cluster 0, represented by diagnostic image with retrospective study, contained emerging topics with great developmental potential and demonstrated high growth rates in recent years. This finding suggests that the topics represented in Cluster 0 may represent future areas of important neurosurgical research. We also found that the developmental trend of China's neurosurgical research is highly correlated with National Natural Science Foundation of China funding acquisition. Conclusions: Co-word analysis and visualization results provided insight into the emerging research topics that are of vital importance, which can be used as a reference by neurosurgeons and researchers for future investigations. In this study, our analysis strategy based on co-word biclustering was able to clearly demonstrate current academic subject development; therefore, co-word biclustering is a reliable bibliometric analysis strategy. ", doi="10.2196/25700", url="https://www.jmir.org/2021/7/e25700", url="http://www.ncbi.nlm.nih.gov/pubmed/36260378" } @Article{info:doi/10.2196/17137, author="An, Ning and Mattison, John and Chen, Xinyu and Alterovitz, Gil", title="Team Science in Precision Medicine: Study of Coleadership and Coauthorship Across Health Organizations", journal="J Med Internet Res", year="2021", month="Jun", day="14", volume="23", number="6", pages="e17137", keywords="precision medicine", keywords="team science", abstract="Background: Interdisciplinary collaborations bring lots of benefits to researchers in multiple areas, including precision medicine. Objective: This viewpoint aims at studying how cross-institution team science would affect the development of precision medicine. Methods: Publications of organizations on the eHealth Catalogue of Activities were collected in 2015 and 2017. The significance of the correlation between coleadership and coauthorship among different organizations was calculated using the Pearson chi-square test of independence. Other nonparametric tests examined whether organizations with coleaders publish more and better papers than organizations without coleaders. Results: A total of 374 publications from 69 organizations were analyzed in 2015, and 7064 papers from 87 organizations were analyzed in 2017. Organizations with coleadership published more papers (P<.001, 2015 and 2017), which received higher citations (Z=--13.547, P<.001, 2017), compared to those without coleadership. Organizations with coleaders tended to publish papers together (P<.001, 2015 and 2017). Conclusions: Our findings suggest that organizations in the field of precision medicine could greatly benefit from institutional-level team science. As a result, stronger collaboration is recommended. ", doi="10.2196/17137", url="https://www.jmir.org/2021/6/e17137", url="http://www.ncbi.nlm.nih.gov/pubmed/34125070" } @Article{info:doi/10.2196/28859, author="Oliveira J e Silva, Lucas and Maldonado, Graciela and Brigham, Tara and Mullan, F. Aidan and Utengen, Audun and Cabrera, Daniel", title="Evaluating Scholars' Impact and Influence: Cross-sectional Study of the Correlation Between a Novel Social Media--Based Score and an Author-Level Citation Metric", journal="J Med Internet Res", year="2021", month="May", day="31", volume="23", number="5", pages="e28859", keywords="social media", keywords="Twitter", keywords="journal impact factor", keywords="h-index", keywords="digital scholarship", keywords="digital platform", keywords="Scopus", keywords="metrics", keywords="scientometrics", keywords="altmetrics", keywords="stakeholders", keywords="health care", keywords="digital health care", abstract="Background: The development of an author-level complementary metric could play a role in the process of academic promotion through objective evaluation of scholars' influence and impact. Objective: The objective of this study was to evaluate the correlation between the Healthcare Social Graph (HSG) score, a novel social media influence and impact metric, and the h-index, a traditional author-level metric. Methods: This was a cross-sectional study of health care stakeholders with a social media presence randomly sampled from the Symplur database in May 2020. We performed stratified random sampling to obtain a representative sample with all strata of HSG scores. We manually queried the h-index in two reference-based databases (Scopus and Google Scholar). Continuous features (HSG score and h-index) from the included profiles were summarized as the median and IQR. We calculated the Spearman correlation coefficients ($\rho$) to evaluate the correlation between the HSG scores and h-indexes obtained from Google Scholar and Scopus. Results: A total of 286 (31.2\%) of the 917 stakeholders had a Google Scholar h-index available. The median HSG score for these profiles was 61.1 (IQR 48.2), and the median h-index was 14.5 (IQR 26.0). For the 286 subjects with the HSG score and Google Scholar h-index available, the Spearman correlation coefficient $\rho$ was 0.1979 (P<.001), indicating a weak positive correlation between these two metrics. A total of 715 (78\%) of 917 stakeholders had a Scopus h-index available. The median HSG score for these profiles was 57.6 (IQR 46.4), and the median h-index was 7 (IQR 16). For the 715 subjects with the HSG score and Scopus h-index available, $\rho$ was 0.2173 (P<.001), also indicating a weak positive correlation. Conclusions: We found a weak positive correlation between a novel author-level complementary metric and the h-index. More than a chiasm between traditional citation metrics and novel social media--based metrics, our findings point toward a bridge between the two domains. ", doi="10.2196/28859", url="https://www.jmir.org/2021/5/e28859", url="http://www.ncbi.nlm.nih.gov/pubmed/34057413" } @Article{info:doi/10.2196/25252, author="Chiang, Lee Austin and Rabinowitz, Galler Loren and Alakbarli, Javid and Chan, W. Walter", title="The Patterns and Impact of Social Media Exposure of Journal Publications in Gastroenterology: Retrospective Cohort Study", journal="J Med Internet Res", year="2021", month="May", day="14", volume="23", number="5", pages="e25252", keywords="social media", keywords="gastroenterology journals", keywords="gastroenterology research", keywords="journal citations", abstract="Background: Medical journals increasingly promote published content through social media platforms such as Twitter. However, gastroenterology journals still rank below average in social media engagement. Objective: We aimed to determine the engagement patterns of publications in gastroenterology journals on Twitter and evaluate the impact of tweets on citations. Methods: This was a retrospective cohort study comparing the 3-year citations of all full-length articles published in five major gastroenterology journals from January 1, 2012, to December 31, 2012, tweeted by official journal accounts with those that were not. Multivariate analysis using linear regression was performed to control for journal impact factor, time since publication, article type, frequency of reposting by other users (``retweets''), and media addition to tweets. Secondary analyses were performed to assess the associations between article type or subtopic and the likelihood of social media promotion/engagement. Results: A total of 1666 articles were reviewed, with 477 tweeted by the official journal account. Tweeting an article independently predicted increased citations after controlling for potential confounders ($\beta$ coefficient=13.09; P=.007). There was significant association between article type and number of retweets on analysis of variance (ANOVA) (P<.001), with guidelines/technical reviews (mean difference 1.04, 95\% CI 0.22-1.87; P<.001) and meta-analyses/systemic reviews (mean difference 1.03, 95\% CI 0.35-1.70; P<.001) being retweeted more than basic science articles. The manuscript subtopics most frequently promoted included motility/functional bowel disease (odds ratio [OR] 3.84, 95\% CI 1.93-7.64; P<.001) and education (OR 4.69, 95\% CI 1.62-13.58; P=.004), while basic science papers were less likely tweeted (OR 0.154, 95\% CI 0.07-0.34; P<.001). Conclusions: Tweeting of gastroenterology journal articles independently predicted higher 3-year citations. Wider adoption of social media to increase reach and measure uptake of published research should be considered. ", doi="10.2196/25252", url="https://www.jmir.org/2021/5/e25252", url="http://www.ncbi.nlm.nih.gov/pubmed/33707166" } @Article{info:doi/10.2196/25379, author="Muric, Goran and Lerman, Kristina and Ferrara, Emilio", title="Gender Disparity in the Authorship of Biomedical Research Publications During the COVID-19 Pandemic: Retrospective Observational Study", journal="J Med Internet Res", year="2021", month="Apr", day="12", volume="23", number="4", pages="e25379", keywords="science of science", keywords="gender disparities", keywords="research evaluation", keywords="COVID-19", abstract="Background: Gender imbalances in academia have been evident historically and persist today. For the past 60 years, we have witnessed the increase of participation of women in biomedical disciplines, showing that the gender gap is shrinking. However, preliminary evidence suggests that women, including female researchers, are disproportionately affected by the COVID-19 pandemic in terms of unequal distribution of childcare, elderly care, and other kinds of domestic and emotional labor. Sudden lockdowns and abrupt shifts in daily routines have had disproportionate consequences on their productivity, which is reflected by a sudden drop in research output in biomedical research, consequently affecting the number of female authors of scientific publications. Objective: The objective of this study is to test the hypothesis that the COVID-19 pandemic has had a disproportionate adverse effect on the productivity of female researchers in the biomedical field in terms of authorship of scientific publications. Methods: This is a retrospective observational bibliometric study. We investigated the proportion of male and female researchers who published scientific papers during the COVID-19 pandemic, using bibliometric data from biomedical preprint servers and selected Springer-Nature journals. We used the ordinary least squares regression model to estimate the expected proportions over time by correcting for temporal trends. We also used a set of statistical methods, such as the Kolmogorov-Smirnov test and regression discontinuity design, to test the validity of the results. Results: A total of 78,950 papers from the bioRxiv and medRxiv repositories and from 62 selected Springer-Nature journals by 346,354 unique authors were analyzed. The acquired data set consisted of papers that were published between January 1, 2019, and August 2, 2020. The proportion of female first authors publishing in the biomedical field during the pandemic dropped by 9.1\%, on average, across disciplines (expected arithmetic mean yest=0.39; observed arithmetic mean y=0.35; standard error of the estimate, Sest=0.007; standard error of the observation, $\sigma$x=0.004). The impact was particularly pronounced for papers related to COVID-19 research, where the proportion of female scientists in the first author position dropped by 28\% (yest=0.39; y=0.28; Sest=0.007; $\sigma$x=0.007). When looking at the last authors, the proportion of women dropped by 7.9\%, on average (yest=0.25; y=0.23; Sest=0.005; $\sigma$x=0.003), while the proportion of women writing about COVID-19 as the last author decreased by 18.8\% (yest=0.25; y=0.21; Sest=0.005; $\sigma$x=0.007). Further, by geocoding authors' affiliations, we showed that the gender disparities became even more apparent when disaggregated by country, up to 35\% in some cases. Conclusions: Our findings document a decrease in the number of publications by female authors in the biomedical field during the global pandemic. This effect was particularly pronounced for papers related to COVID-19, indicating that women are producing fewer publications related to COVID-19 research. This sudden increase in the gender gap was persistent across the 10 countries with the highest number of researchers. These results should be used to inform the scientific community of this worrying trend in COVID-19 research and the disproportionate effect that the pandemic has had on female academics. ", doi="10.2196/25379", url="https://www.jmir.org/2021/4/e25379", url="http://www.ncbi.nlm.nih.gov/pubmed/33735097" } @Article{info:doi/10.2196/23703, author="Abd-Alrazaq, Alaa and Schneider, Jens and Mifsud, Borbala and Alam, Tanvir and Househ, Mowafa and Hamdi, Mounir and Shah, Zubair", title="A Comprehensive Overview of the COVID-19 Literature: Machine Learning--Based Bibliometric Analysis", journal="J Med Internet Res", year="2021", month="Mar", day="8", volume="23", number="3", pages="e23703", keywords="novel coronavirus disease", keywords="COVID-19", keywords="SARS-CoV-2", keywords="2019-nCoV", keywords="bibliometric analysis", keywords="literature", keywords="machine learning", keywords="research", keywords="review", abstract="Background: Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19--related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. Objective: We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. Methods: We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning--based method to analyze the most relevant COVID-19--related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub. Results: Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19--related articles was China (2950/17,270, 17.08\%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread. Conclusions: We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors. ", doi="10.2196/23703", url="https://www.jmir.org/2021/3/e23703", url="http://www.ncbi.nlm.nih.gov/pubmed/33600346" } @Article{info:doi/10.2196/21408, author="Tornberg, N. Haley and Moezinia, Carine and Wei, Chapman and Bernstein, A. Simone and Wei, Chaplin and Al-Beyati, Refka and Quan, Theodore and Diemert, J. David", title="Retracted: ``Assessing the Dissemination of COVID-19 Articles Across Social Media With Altmetric and PlumX Metrics: Correlational Study''", journal="J Med Internet Res", year="2021", month="Jan", day="14", volume="23", number="1", pages="e21408", keywords="Altmetric", keywords="PlumX", keywords="social media", keywords="impact factor", keywords="COVID-19", keywords="information", keywords="dissemination", keywords="citation", abstract="Background: The use of social media assists in the distribution of COVID-19 information to the general public and health professionals. Alternative-level metrics (ie, altmetrics) and PlumX metrics are new bibliometrics that can assess how many times a scientific article has been shared and how much a scientific article has spread within social media platforms. Objective: Our objective was to characterize and compare the traditional bibliometrics (ie, citation count and impact factors) and new bibliometrics (ie, Altmetric Attention Score [AAS] and PlumX score) of the top 100 COVID-19 articles with the highest AASs. Methods: The top 100 articles with highest AASs were identified with Altmetric Explorer in May 2020. The AASs, journal names, and the number of mentions in various social media databases of each article were collected. Citation counts and PlumX Field-Weighted Citation Impact scores were collected from the Scopus database. Additionally, AASs, PlumX scores, and citation counts were log-transformed and adjusted by +1 for linear regression, and Spearman correlation coefficients were used to determine correlations. Results: The median AAS, PlumX score, and citation count were 4922.50, 37.92, and 24.00, respectively. The New England Journal of Medicine published the most articles (18/100, 18\%). The highest number of mentions (985,429/1,022,975, 96.3\%) were found on Twitter, making it the most frequently used social media platform. A positive correlation was observed between AAS and citation count (r2=0.0973; P=.002), and between PlumX score and citation count (r2=0.8911; P<.001). Conclusions: Our study demonstrated that citation count weakly correlated with AASs and strongly correlated with PlumX scores, with regard to COVID-19 articles at this point in time. Altmetric and PlumX metrics should be used to complement traditional citation counts when assessing the dissemination and impact of a COVID-19 article. ", doi="10.2196/21408", url="http://www.jmir.org/2021/1/e21408/", url="http://www.ncbi.nlm.nih.gov/pubmed/33406049" } @Article{info:doi/10.2196/22327, author="Oska, Sandra and Lerma, Edgar and Topf, Joel", title="A Picture Is Worth a Thousand Views: A Triple Crossover Trial of Visual Abstracts to Examine Their Impact on Research Dissemination", journal="J Med Internet Res", year="2020", month="Dec", day="4", volume="22", number="12", pages="e22327", keywords="social media", keywords="science communication", keywords="visual abstract", keywords="Twitter", keywords="dissemination", abstract="Background: A visual abstract is a graphic summary of a research article's question, methods, and major findings. Although they have a number of uses, visual abstracts are chiefly used to promote research articles on social media. Objective: This study aimed to determine if the use of visual abstracts increases the visibility of nephrology research shared on Twitter. Methods: A prospective case-control crossover study was conducted using 40 research articles published in the American Journal of Nephrology (AJN). Each article was shared by the AJN Twitter account in 3 formats: (1) the article citation, (2) the citation with a key figure from the article, and (3) the citation with a visual abstract. Tweets were spaced 2 weeks apart to allow washout of the previous tweet, and the order of the tweets was randomized. Dissemination was measured via retweets, views, number of link clicks, and Altmetric scores. Results: Tweets that contained a visual abstract had more than twice as many views as citation-only tweets (1351, SD 1053 vs 639, SD 343) and nearly twice as many views as key figure tweets (1351, SD 1053 vs 732, SD 464). Visual abstract tweets had 5 times the engagements of citation-only tweets and more than 3.5 times the engagements of key figure tweets. Visual abstract tweets were also associated with greater increases in Altmetric scores as compared to citation-only tweets (2.20 vs 1.05). Conclusions: The use of visual abstracts increased visibility of research articles on Twitter, resulting in a greater number of views, engagements, and retweets. Visual abstracts were also associated with increased Altmetric scores as compared to citation-only tweets. These findings support the broader use of visual abstracts in the scientific community. Journals should consider visual abstracts as valuable tools for research dissemination. ", doi="10.2196/22327", url="https://www.jmir.org/2020/12/e22327", url="http://www.ncbi.nlm.nih.gov/pubmed/33275112" } @Article{info:doi/10.2196/23724, author="Wei, Chapman and Fong, Aaron and Quan, Theodore and Gupta, Puneet and Friedman, Adam", title="Assessment of Altmetrics and PlumX Metrics Scoring as Mechanisms to Evaluate the Top 100 Trending Hidradenitis Suppurativa Articles on Social Media: Cross-Sectional Study", journal="JMIR Dermatol", year="2020", month="Nov", day="19", volume="3", number="1", pages="e23724", keywords="altmetric", keywords="PlumX", keywords="social media", keywords="impact factor", keywords="hidradenitis suppurativa", abstract="Background: Dermatologists are increasingly utilizing social media platforms to disseminate scientific information. New tools, such as altmetrics and PlumX metrics, have been made available to rapidly capture the level of scientific article dissemination across social media platforms. However, no studies have been performed to assess the level of scientific article dissemination across social media regarding hidradenitis suppurativa, a disease that is still currently not well understood. Objective: The aim of our study was to evaluate the utility of altmetrics and PlumX metrics by characterizing the top 100 ``trending'' hidradenitis suppurativa articles in the altmetric database by the altmetric attention score and PlumX score. Methods: Altmetric data components of the top 100 hidradenitis suppurativa articles were extracted from the altmetric database. Article citation count was found using Web of Science. PlumX field-weighted impact scores for each article were collected from the Scopus database. Journal title, open-access status, article type, and study design of original articles were assessed. Additionally, the altmetric attention score, PlumX score, and citation count were log transformed and adjusted by +1 for linear regression, and Spearman correlation coefficients were utilized to determine correlations. Results: Most of the top 100 ``trending'' hidradenitis suppurativa articles were published in JAMA Dermatology (n=27, 27\%). The median altmetric attention score, PlumX score, and citation count were 25.5, 3.7, and 10.5, respectively. The most mentions regarding social media platforms came from Twitter. Although no correlation was observed between the citation count and altmetric attention score (r2=0.019, P=.17), positive correlation was observed between the citation count and PlumX score (r2=0.469, P<.001). Conclusions: Our research demonstrated that citation count is not correlated with the altmetric attention score, but is strongly correlated with the PlumX score regarding hidradenitis suppurativa articles at this point in time. With the continual increase of social media usage by medical professionals and researchers, this study can help investigators understand the best way to captivate their audience. ", doi="10.2196/23724", url="http://derma.jmir.org/2020/1/e23724/" } @Article{info:doi/10.2196/15607, author="Bardus, Marco and El Rassi, Rola and Chahrour, Mohamad and Akl, W. Elie and Raslan, Sattar Abdul and Meho, I. Lokman and Akl, A. Elie", title="The Use of Social Media to Increase the Impact of Health Research: Systematic Review", journal="J Med Internet Res", year="2020", month="Jul", day="6", volume="22", number="7", pages="e15607", keywords="social media", keywords="research", keywords="bibliometrics", keywords="Altmetrics", keywords="journal impact factor", keywords="translational medical research", abstract="Background: Academics in all disciplines increasingly use social media to share their publications on the internet, reaching out to different audiences. In the last few years, specific indicators of social media impact have been developed (eg, Altmetrics), to complement traditional bibliometric indicators (eg, citation count and h-index). In health research, it is unclear whether social media impact also translates into research impact. Objective: The primary aim of this study was to systematically review the literature on the impact of using social media on the dissemination of health research. The secondary aim was to assess the correlation between Altmetrics and traditional citation-based metrics. Methods: We conducted a systematic review to identify studies that evaluated the use of social media to disseminate research published in health-related journals. We specifically looked at studies that described experimental or correlational studies linking the use of social media with outcomes related to bibliometrics. We searched the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases using a predefined search strategy (International Prospective Register of Systematic Reviews: CRD42017057709). We conducted independent and duplicate study selection and data extraction. Given the heterogeneity of the included studies, we summarized the findings through a narrative synthesis. Results: Of a total of 18,624 retrieved citations, we included 51 studies: 7 (14\%) impact studies (answering the primary aim) and 44 (86\%) correlational studies (answering the secondary aim). Impact studies reported mixed results with several limitations, including the use of interventions of inappropriately low intensity and short duration. The majority of correlational studies suggested a positive association between traditional bibliometrics and social media metrics (eg, number of mentions) in health research. Conclusions: We have identified suggestive yet inconclusive evidence on the impact of using social media to increase the number of citations in health research. Further studies with better design are needed to assess the causal link between social media impact and bibliometrics. ", doi="10.2196/15607", url="https://www.jmir.org/2020/7/e15607", url="http://www.ncbi.nlm.nih.gov/pubmed/32628113" } @Article{info:doi/10.2196/16739, author="Li, Xin and Rousseau, F. Justin and Ding, Ying and Song, Min and Lu, Wei", title="Understanding Drug Repurposing From the Perspective of Biomedical Entities and Their Evolution: Bibliographic Research Using Aspirin", journal="JMIR Med Inform", year="2020", month="Jun", day="16", volume="8", number="6", pages="e16739", keywords="drug repurposing", keywords="biomedical entities", keywords="entitymetrics", keywords="bibliometrics", keywords="aspirin", keywords="acetylsalicylic acid", abstract="Background: Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research. Objective: The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution. Methods: In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P1), Promising Index (P2), Prestige Index (P3), and Collaboration Index (CI). Results: We found that the maxima of P1, P3, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P1 and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P2 was more sensitive to immediate changes. P3 reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug. Conclusions: In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development. ", doi="10.2196/16739", url="https://medinform.jmir.org/2020/6/e16739", url="http://www.ncbi.nlm.nih.gov/pubmed/32543442" } @Article{info:doi/10.2196/12288, author="Sathianathen, Jude Niranjan and Lane III, Robert and Murphy, G. Declan and Loeb, Stacy and Bakker, Caitlin and Lamb, D. Alastair and Weight, J. Christopher", title="Social Media Coverage of Scientific Articles Immediately After Publication Predicts Subsequent Citations - \#SoME\_Impact Score: Observational Analysis", journal="J Med Internet Res", year="2020", month="Apr", day="17", volume="22", number="4", pages="e12288", keywords="bibliometrics", keywords="online social networking", keywords="online systems", keywords="online intervention", keywords="social media", abstract="Background: Social media coverage is increasingly used to spread the message of scientific publications. Traditionally, the scientific impact of an article is measured by the number of citations. At a journal level, this conventionally matures over a 2-year period, and it is challenging to gauge impact around the time of publication. Objective: We, therefore, aimed to assess whether Web-based attention is associated with citations and to develop a predictive model that assigns relative importance to different elements of social media coverage: the \#SoME\_Impact score. Methods: We included all original articles published in 2015 in a selection of the highest impact journals: The New England Journal of Medicine, The Lancet, the Journal of the American Medical Association, Nature, Cell, and Science. We first characterized the change in Altmetric score over time by taking a single month's sample of recently published articles from the same journals and gathered Altmetric data daily from the time of publication to create a mixed effects spline model. We then obtained the overall weighted Altmetric score for all articles from 2015, the unweighted data for each Altmetric component, and the 2-year citation count from Scopus for each of these articles from 2016 to 2017. We created a stepwise multivariable linear regression model to develop a \#SoME\_Score that was predictive of 2-year citations. The score was validated using a dataset of articles from the same journals published in 2016. Results: In our unselected sample of 145 recently published articles, social media coverage appeared to plateau approximately 14 days after publication. A total of 3150 articles with a median citation count of 16 (IQR 5-33) and Altmetric score of 72 (IQR 28-169) were included for analysis. On multivariable regression, compared with articles in the lowest quantile of \#SoME\_Score, articles in the second, third, and upper quantiles had 0.81, 15.20, and 87.67 more citations, respectively. On the validation dataset, \#SoME\_Score model outperformed the Altmetric score (adjusted R2 0.19 vs 0.09; P<.001). Articles in the upper quantile of \#SoME\_Score were more than 5 times more likely to be among the upper quantile of those cites (odds ratio 5.61, 95\% CI 4.70-6.73). Conclusions: Social media attention predicts citations and could be used as an early surrogate measure of scientific impact. Owing to the cross-sectional study design, we cannot determine whether correlation relates to causation. ", doi="10.2196/12288", url="https://www.jmir.org/2020/4/e12288", url="http://www.ncbi.nlm.nih.gov/pubmed/32301733" } @Article{info:doi/10.2196/15643, author="Murray, Gregg and Hellen, Rebecca and Ralph, James and Ni Raghallaigh, Siona", title="Comparison of Traditional Citation Metrics and Altmetrics Among Dermatology Journals: Content and Correlational Analysis Study", journal="JMIR Dermatol", year="2020", month="Feb", day="12", volume="3", number="1", pages="e15643", keywords="dermatology", keywords="altmetrics", keywords="impact factor", keywords="citations", keywords="medical informatics", abstract="Background: Research impact has traditionally been measured using citation count and impact factor (IF). Academics have long relied heavily on this form of metric system to measure a publication's impact. A higher number of citations is viewed as an indicator of the importance of the research and a marker for the impact of the publishing journal. Recently, social media and online news sources have become important avenues for dissemination of research, resulting in the emergence of an alternative metric system known as altmetrics. Objective: We assessed the correlation between altmetric attention score (AAS) and traditional scientific impact markers, namely journal IF and article citation count, for all the dermatology journal and published articles of 2017. Methods: We identified dermatology journals and their associated IFs available in 2017 using InCites Journal Citation Reports. We entered all 64 official dermatology journals into Altmetric Explorer, a Web-based platform that enables users to browse and report on all attention data for every piece of scholarly content for which Altmetric Explorer has found attention. Results: For the 64 dermatology journals, there was a moderate positive correlation between journal IF and journal AAS (rs=.513, P<.001). In 2017, 6323 articles were published in the 64 dermatology journals. Our data show that there was a weak positive correlation between the traditional article citation count and AAS (rs=.257, P<.001). Conclusions: Our data show a weak correlation between article citation count and AAS. Temporal factors may explain this weak association. Newer articles may receive increased online attention after publication, while it may take longer for scientific citation counts to accumulate. Stories that are at times deemed newsworthy and then disseminated across the media and social media platforms border on sensationalism and may not be truly academic in nature. The opposite can also be true. ", doi="10.2196/15643", url="https://derma.jmir.org/2020/1/e15643" } @Article{info:doi/10.2196/15511, author="Tran, Xuan Bach and Nghiem, Son and Sahin, Oz and Vu, Manh Tuan and Ha, Hai Giang and Vu, Thu Giang and Pham, Quang Hai and Do, Thi Hoa and Latkin, A. Carl and Tam, Wilson and Ho, H. Cyrus S. and Ho, M. Roger C.", title="Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study", journal="J Med Internet Res", year="2019", month="Nov", day="1", volume="21", number="11", pages="e15511", keywords="artificial intelligence", keywords="applications", keywords="medicine", keywords="scientometric", keywords="bibliometric", keywords="latent Dirichlet allocation", abstract="Background: Artificial intelligence (AI)--based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. Objective: This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. Methods: We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. Results: The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. Conclusions: The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries. ", doi="10.2196/15511", url="https://www.jmir.org/2019/11/e15511", url="http://www.ncbi.nlm.nih.gov/pubmed/31682577" } @Article{info:doi/10.2196/jmir.9368, author="Araujo, Costa Amanda and Nascimento, Port Dafne and Gonzalez, Zoldan Gabrielle and Maher, G. Christopher and Costa, Pena Leonardo Oliveira", title="Impact of Low Back Pain Clinical Trials Measured by the Altmetric Score: Cross-Sectional Study", journal="J Med Internet Res", year="2018", month="Apr", day="05", volume="20", number="4", pages="e86", keywords="Altmetric", keywords="social impact", keywords="clinical trials", keywords="low back pain", abstract="Background: There is interest from authors and publishers in sharing the results of their studies over the Internet in order to increase their readership. In this way, articles tend to be discussed and the impact of these articles tends to be increased. In order to measure this type of impact, a new score (named Altmetric) was created. Altmetric aims to understand the individual impact of each article through the attention attracted online. Objective: The primary objective of this study was to analyze potential factors related with the publishing journal and the publishing trial that could be associated with Altmetric scores on a random sample of low back pain randomized controlled trials (RCTs). The secondary objective of this study was to describe the characteristics of these trials and their Altmetric scores. Methods: We searched for all low back pain RCTs indexed on the Physiotherapy Evidence Database (PEDro; www.pedro.org.au) published between 2010 and 2015. A total of 200 articles were randomly selected, and we extracted data related to the publishing trial, the publishing journal, methodological quality of the trials (measured by the 0-10 item PEDro scale), and total and individual scores of Altmetric mentioned and Altmetric reader. The study was a cross-sectional study, and multivariate regression models and descriptive statistics were used. Results: A total of four variables were associated with Altmetric mentioned score: impact factor ($\beta$-coefficient=3.4 points), number of years since publication ($\beta$-coefficient=--4.9 points), number of citations divided by years since publication ($\beta$-coefficient=5.2 points), and descriptive title ($\beta$-coefficient=--29.4 points). Only one independent variable was associated with Altmetric reader score: number of citations divided by years since publication ($\beta$-coefficient=10.1 points, 95\% CI 7.74-12.46). We also found that the majority of articles were published in English, with a descriptive title, and published in open access journals endorsing the Consolidated Standards of Reporting Trials (CONSORT) statement. Conclusions: Researchers should preferably select high impact factor journals for submission and use declarative or interrogative titles, as these factors are likely to increase the visibility of their studies in social media. ", doi="10.2196/jmir.9368", url="http://www.jmir.org/2018/4/e86/", url="http://www.ncbi.nlm.nih.gov/pubmed/29622526" } @Article{info:doi/10.2196/jmir.3326, author="Li, Fan and Li, Min and Guan, Peng and Ma, Shuang and Cui, Lei", title="Mapping Publication Trends and Identifying Hot Spots of Research on Internet Health Information Seeking Behavior: A Quantitative and Co-Word Biclustering Analysis", journal="J Med Internet Res", year="2015", month="Mar", day="25", volume="17", number="3", pages="e81", keywords="information seeking behavior", keywords="Internet", keywords="health information", keywords="bibliometric analysis", keywords="co-word analysis", keywords="biclustering", keywords="hot spots", keywords="publication status", abstract="Background: The Internet has become an established source of health information for people seeking health information. In recent years, research on the health information seeking behavior of Internet users has become an increasingly important scholarly focus. However, there have been no long-term bibliometric studies to date on Internet health information seeking behavior. Objective: The purpose of this study was to map publication trends and explore research hot spots of Internet health information seeking behavior. Methods: A bibliometric analysis based on PubMed was conducted to investigate the publication trends of research on Internet health information seeking behavior. For the included publications, the annual publication number, the distribution of countries, authors, languages, journals, and annual distribution of highly frequent major MeSH (Medical Subject Headings) terms were determined. Furthermore, co-word biclustering analysis of highly frequent major MeSH terms was utilized to detect the hot spots in this field. Results: A total of 533 publications were included. The research output was gradually increasing. There were five authors who published four or more articles individually. A total of 271 included publications (50.8\%) were written by authors from the United States, and 516 of the 533 articles (96.8\%) were published in English. The eight most active journals published 34.1\% (182/533) of the publications on this topic. Ten research hot spots were found: (1) behavior of Internet health information seeking about HIV infection or sexually transmitted diseases, (2) Internet health information seeking behavior of students, (3) behavior of Internet health information seeking via mobile phone and its apps, (4) physicians' utilization of Internet medical resources, (5) utilization of social media by parents, (6) Internet health information seeking behavior of patients with cancer (mainly breast cancer), (7) trust in or satisfaction with Web-based health information by consumers, (8) interaction between Internet utilization and physician-patient communication or relationship, (9) preference and computer literacy of people using search engines or other Web-based systems, and (10) attitude of people (especially adolescents) when seeking health information via the Internet. Conclusions: The 10 major research hot spots could provide some hints for researchers when launching new projects. The output of research on Internet health information seeking behavior is gradually increasing. Compared to the United States, the relatively small number of publications indexed by PubMed from other developed and developing countries indicates to some extent that the field might be still underdeveloped in many countries. More studies on Internet health information seeking behavior could give some references for health information providers. ", doi="10.2196/jmir.3326", url="http://www.jmir.org/2015/3/e81/", url="http://www.ncbi.nlm.nih.gov/pubmed/25830358" } @Article{info:doi/10.2196/jmir.3871, author="Moseley, T. Edward and Hsu, J. Douglas and Stone, J. David and Celi, Anthony Leo", title="Beyond Open Big Data: Addressing Unreliable Research", journal="J Med Internet Res", year="2014", month="Nov", day="11", volume="16", number="11", pages="e259", keywords="open data", keywords="unreliable research", keywords="collaborative learning", keywords="knowledge discovery", keywords="peer review", keywords="research culture", doi="10.2196/jmir.3871", url="http://www.jmir.org/2014/11/e259/", url="http://www.ncbi.nlm.nih.gov/pubmed/25405277" } @Article{info:doi/10.2196/ijmr.2748, author="Yavnai, Nirit and Huerta-Hartal, Michael and Mimouni, Francis and Pinkert, Moshe and Dagan, David and Kreiss, Yitshak", title="Military Medicine Publications: What has Happened in the Past Two Decades?", journal="Interact J Med Res", year="2014", month="May", day="28", volume="3", number="2", pages="e10", keywords="military medicine", keywords="publication types", keywords="trend", abstract="Background: Military medical personnel, like all other physician specialists, face the challenge of keeping updated with developments in their field of expertise, in view of the great amount of new medical information published in the literature. The availability of the Internet has triggered tremendous changes in publication characteristics, and in some fields, the number of publications has increased substantially. The emergence of electronic open access journals and the improvement in Web search engines has triggered a significant change in the publication processes and in accessibility of information. Objective: The objective of this study was to characterize the temporal trends in the number and types of publications in military medicine in the medical literature. Methods: We searched all PubMed-registered publications from January 1, 1990 to December 31, 2010 using the keywords ``military'' or ``army''. We used the publication tag in PubMed to identify and examine major publication types. The trends were tested using the Mann-Kendall test for trend. Results: Our search yielded 44,443 publications in military medicine during the evaluation period. Overall, the number of publications showed two distinct phases over time: (1) a moderate increase from 1990 to 2001 with a mean annual increase of 2.78\% (r2=.79, P<.002), and (2) a steeper mean annual increase of 11.20\% (r2=.96, P<.002) from 2002 to 2010. Most of the examined publication types showed a similar pattern. The proportion of high-quality-of-evidence publication types (randomized controlled trials, systematic reviews, and meta-analyses) increased from 2.91\% to 8.43\% of the overall military medicine publications with a mean annual incremental increase of 14.20\%. These publication types demonstrated a similar dual phase pattern of increase (10.01\%, r2=.80, P<.002 for 1990-2001 and 20.66\%, r2=.88, P<.002 for 2002-2010). Conclusions: We conclude that over the past twenty years, scholarly work in the field of military medicine has shown a significant increase in volume, particularly among high quality publication types. However, practice guidelines remain rare, and meta-analyses are still limited in number. ", doi="10.2196/ijmr.2748", url="http://www.i-jmr.org/2014/2/e10/", url="http://www.ncbi.nlm.nih.gov/pubmed/24870264" } @Article{info:doi/10.2196/jmir.2707, author="Liu, Li Chun and Xu, Quan Yue and Wu, Hui and Chen, Si Si and Guo, Jun Ji", title="Correlation and Interaction Visualization of Altmetric Indicators Extracted From Scholarly Social Network Activities: Dimensions and Structure", journal="J Med Internet Res", year="2013", month="Nov", day="25", volume="15", number="11", pages="e259", keywords="altmetrics", keywords="article-level metrics", keywords="scholarly social network tools", keywords="indicator", keywords="dimension", keywords="structure", abstract="Background: Citation counts for peer-reviewed articles and the impact factor of journals have long been indicators of article importance or quality. In the Web 2.0 era, growing numbers of scholars are using scholarly social network tools to communicate scientific ideas with colleagues, thereby making traditional indicators less sufficient, immediate, and comprehensive. In these new situations, the altmetric indicators offer alternative measures that reflect the multidimensional nature of scholarly impact in an immediate, open, and individualized way. In this direction of research, some studies have demonstrated the correlation between altmetrics and traditional metrics with different samples. However, up to now, there has been relatively little research done on the dimension and interaction structure of altmetrics. Objective: Our goal was to reveal the number of dimensions that altmetric indicators should be divided into and the structure in which altmetric indicators interact with each other. Methods: Because an article-level metrics dataset is collected from scholarly social media and open access platforms, it is one of the most robust samples available to study altmetric indicators. Therefore, we downloaded a large dataset containing activity data in 20 types of metrics present in 33,128 academic articles from the application programming interface website. First, we analyzed the correlation among altmetric indicators using Spearman rank correlation. Second, we visualized the multiple correlation coefficient matrixes with graduated colors. Third, inputting the correlation matrix, we drew an MDS diagram to demonstrate the dimension for altmetric indicators. For correlation structure, we used a social network map to represent the social relationships and the strength of relations. Results: We found that the distribution of altmetric indicators is significantly non-normal and positively skewed. The distribution of downloads and page views follows the Pareto law. Moreover, we found that the Spearman coefficients from 91.58\% of the pairs of variables indicate statistical significance at the .01 level. The non-metric MDS map divided the 20 altmetric indicators into three clusters: traditional metrics, active altmetrics, and inactive altmetrics. The social network diagram showed two subgroups that are tied to each other but not to other groups, thus indicating an intersection between altmetrics and traditional metric indicators. Conclusions: Altmetrics complement, and most correlate significantly with, traditional measures. Therefore, in future evaluations of the social impact of articles, we should consider not only traditional metrics but also active altmetrics. There may also be a transfer phenomenon for the social impact of academic articles. The impact transfer path has transfer, or intermediate, stations that transport and accelerate article social impact from active altmetrics to traditional metrics and vice versa. This discovery will be helpful to explain the impact transfer mechanism of articles in the Web 2.0 era. Hence, altmetrics are in fact superior to traditional filters for assessing scholarly impact in multiple dimensions and in terms of social structure. ", doi="10.2196/jmir.2707", url="http://www.jmir.org/2013/11/e259/", url="http://www.ncbi.nlm.nih.gov/pubmed/24275693" } @Article{info:doi/10.2196/jmir.2177, author="El Emam, Khaled and Arbuckle, Luk and Jonker, Elizabeth and Anderson, Kevin", title="Two h-Index Benchmarks for Evaluating the Publication Performance of Medical Informatics Researchers", journal="J Med Internet Res", year="2012", month="Oct", day="18", volume="14", number="5", pages="e144", keywords="h-Index", keywords="medical informatics", keywords="bibliometrics", keywords="evaluation", keywords="research output", abstract="Background: The h-index is a commonly used metric for evaluating the publication performance of researchers. However, in a multidisciplinary field such as medical informatics, interpreting the h-index is a challenge because researchers tend to have diverse home disciplines, ranging from clinical areas to computer science, basic science, and the social sciences, each with different publication performance profiles. Objective: To construct a reference standard for interpreting the h-index of medical informatics researchers based on the performance of their peers. Methods: Using a sample of authors with articles published over the 5-year period 2006--2011 in the 2 top journals in medical informatics (as determined by impact factor), we computed their h-index using the Scopus database. Percentiles were computed to create a 6-level benchmark, similar in scheme to one used by the US National Science Foundation, and a 10-level benchmark. Results: The 2 benchmarks can be used to place medical informatics researchers in an ordered category based on the performance of their peers. A validation exercise mapped the benchmark levels to the ranks of medical informatics academic faculty in the United States. The 10-level benchmark tracked academic rank better (with no ties) and is therefore more suitable for practical use. Conclusions: Our 10-level benchmark provides an objective basis to evaluate and compare the publication performance of medical informatics researchers with that of their peers using the h-index. ", doi="10.2196/jmir.2177", url="http://www.jmir.org/2012/5/e144/", url="http://www.ncbi.nlm.nih.gov/pubmed/23079075" } @Article{info:doi/10.2196/jmir.2012, author="Eysenbach, Gunther", title="Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact", journal="J Med Internet Res", year="2011", month="Dec", day="16", volume="13", number="4", pages="e123", keywords="bibliometrics", keywords="blogging", keywords="periodicals as topic", keywords="peer-review", keywords="publishing", keywords="social media analytics", keywords="scientometrics", keywords="infodemiology", keywords="infometrics", keywords="reproducibility of results", keywords="medicine 2.0", keywords="power law", keywords="Twitter", abstract="Background: Citations in peer-reviewed articles and the impact factor are generally accepted measures of scientific impact. Web 2.0 tools such as Twitter, blogs or social bookmarking tools provide the possibility to construct innovative article-level or journal-level metrics to gauge impact and influence. However, the relationship of the these new metrics to traditional metrics such as citations is not known. Objective: (1) To explore the feasibility of measuring social impact of and public attention to scholarly articles by analyzing buzz in social media, (2) to explore the dynamics, content, and timing of tweets relative to the publication of a scholarly article, and (3) to explore whether these metrics are sensitive and specific enough to predict highly cited articles. Methods: Between July 2008 and November 2011, all tweets containing links to articles in the Journal of Medical Internet Research (JMIR) were mined. For a subset of 1573 tweets about 55 articles published between issues 3/2009 and 2/2010, different metrics of social media impact were calculated and compared against subsequent citation data from Scopus and Google Scholar 17 to 29 months later. A heuristic to predict the top-cited articles in each issue through tweet metrics was validated. Results: A total of 4208 tweets cited 286 distinct JMIR articles. The distribution of tweets over the first 30 days after article publication followed a power law (Zipf, Bradford, or Pareto distribution), with most tweets sent on the day when an article was published (1458/3318, 43.94\% of all tweets in a 60-day period) or on the following day (528/3318, 15.9\%), followed by a rapid decay. The Pearson correlations between tweetations and citations were moderate and statistically significant, with correlation coefficients ranging from .42 to .72 for the log-transformed Google Scholar citations, but were less clear for Scopus citations and rank correlations. A linear multivariate model with time and tweets as significant predictors (P < .001) could explain 27\% of the variation of citations. Highly tweeted articles were 11 times more likely to be highly cited than less-tweeted articles (9/12 or 75\% of highly tweeted article were highly cited, while only 3/43 or 7\% of less-tweeted articles were highly cited; rate ratio 0.75/0.07 = 10.75, 95\% confidence interval, 3.4--33.6). Top-cited articles can be predicted from top-tweeted articles with 93\% specificity and 75\% sensitivity. Conclusions: Tweets can predict highly cited articles within the first 3 days of article publication. Social media activity either increases citations or reflects the underlying qualities of the article that also predict citations, but the true use of these metrics is to measure the distinct concept of social impact. Social impact measures based on tweets are proposed to complement traditional citation metrics. The proposed twimpact factor may be a useful and timely metric to measure uptake of research findings and to filter research findings resonating with the public in real time. ", doi="10.2196/jmir.2012", url="http://www.jmir.org/2011/4/e123/", url="http://www.ncbi.nlm.nih.gov/pubmed/22173204" }