Journal of Medical Internet Research

Ulcerative colitis (UC) is a chronic disease characterized by frequent relapses, requiring long-term management and consuming substantial medical and social resources. Effective management of UC remains challenging due to the need for sustainable remission strategies, continuity of care, and access to medical services. Intelligent diagnosis refers to the use of artificial intelligence–driven algorithms to analyze patient-reported symptoms, generate diagnostic probabilities, and provide treatment recommendations through interactive tools. This approach could potentially function as a method for UC management.

Chronic lung disease (CLD) is one of the most prevalent noncommunicable diseases globally, significantly burdening patients and increasing health care expenditures. Digital health education (DHE) is increasingly important in chronic disease prevention and management. However, DHE characteristics and impacts in CLD are rarely reported.

Most artificial intelligence–based research on acute kidney injury (AKI) prediction has focused on intensive care unit settings, limiting their generalizability to general wards. The lack of standardized AKI definitions and reliance on intensive care units further hinder the clinical applicability of these models.

Named entity recognition (NER) plays a vital role in extracting critical medical entities from health care records, facilitating applications such as clinical decision support and data mining. Developing robust NER models for low-resource languages, such as Estonian, remains a challenge due to the scarcity of annotated data and domain-specific pretrained models. Large language models (LLMs) have proven to be promising in understanding text from any language or domain.

Monitoring the emotional states of patients with psychiatric problems has always been challenging due to the noncontinuous nature of clinical assessments, the effect of the health care environment, and the inherent subjectivity of evaluation instruments. However, mental states in psychiatric disorders exhibit substantial variability over time, making real-time monitoring crucial for preventing risky situations and ensuring appropriate treatment.

Research regarding the effectiveness of digital health interventions (DHIs) for people living with chronic pain is widely documented, although it is often measured against changes in clinical outcomes. To gain a comprehensive understanding of the full impact of DHIs, it is vital to understand the experience of individuals who are using them. An exploration of qualitative data regarding the experience of people living with chronic pain engaging with DHIs could provide a more in-depth account of how individuals interact and engage with such tools, uncovering the overall impact DHIs can have on the lives of people living with chronic pain.

The use of structured electronic health records in health care systems has grown rapidly. These systems collect huge amounts of patient information, including diagnosis codes representing temporal medical history. Sequential diagnostic information has proven valuable for predicting patient outcomes. However, the extent to which these types of data have been incorporated into deep learning (DL) models has not been examined.

High-quality data are critical in health care, forming the cornerstone for accurate diagnoses, effective treatment plans, and reliable conclusions. Similarly, high-quality datasets underpin the development and performance of large language models (LLMs). Among these, instruction-tuning datasets (ITDs) used for instruction fine-tuning have been pivotal in enhancing LLM performance and generalization capabilities across diverse tasks. This tutorial provides a comprehensive guide to designing, creating, and evaluating ITDs for health care applications. Written from a clinical perspective, it aims to make the concepts accessible to a broad audience, especially medical practitioners. Key topics include identifying useful data sources, defining the characteristics of well-designed datasets, and crafting high-quality instruction-input-output examples. We explore practical approaches to dataset construction, examining the advantages and limitations of 3 primary methods: fully manual preparation by expert annotators, fully synthetic generation using artificial intelligence (AI), and an innovative hybrid approach in which experts draft the initial dataset and AI generates additional data. Moreover, we discuss strategies for metadata selection and human evaluation to ensure the quality and effectiveness of ITDs. By integrating these elements, this tutorial provides a structured framework for establishing ITDs. It bridges technical and clinical domains, supporting the continued interdisciplinary advancement of AI in medicine. Additionally, we address the limitations of current practices and propose future directions, emphasizing the need for a global, unified framework for ITDs. We also argue that artificial general intelligence (AGI), if realized, will not replace empirical research in medicine. AGI will depend on human-curated datasets to process and apply medical knowledge. At the same time, ITDs will likely remain the most effective method of supplying this knowledge to AGI, positioning them as a critical tool in AI-driven health care.

Measurement-based care improves patient outcomes by using standardized scales, but its widespread adoption is hindered by the lack of accessible and structured knowledge, particularly in unstructured Chinese medical literature. Extracting scale-related knowledge entities from these texts is challenging due to limited annotated data. While large language models (LLMs) show promise in named entity recognition (NER), specialized prompting strategies are needed to accurately recognize medical scale-related entities, especially in low-resource settings.

Psychiatric advance directives (PAD), also known as advance statements or advance choice documents, are legal documents that enable people with mental health conditions to specify their treatment preferences in advance for possible future crises. Subtypes of PADs include crisis cards, joint crisis plans, and self-binding directives (also known as Ulysses contracts). These instruments are intended to improve service user involvement and need orientation in the care of mental crises and to avoid traumatization through unwanted treatment. The existing evidence suggests that people who complete a PAD tend to work more cooperatively with their clinician and experience fewer involuntary hospital admissions. Nevertheless, PADs have not been successfully mainstreamed into care due to multiple barriers to the implementation of PADs, mainly around the completion of PADs and their accessibility and use in crises. The reasons for this include the lack of support in the completion process and acceptance problems, especially on the part of professionals. The research to date primarily recommends support for service users from facilitators, such as peer support workers, and training for all stakeholders. In this article, we argue that while these approaches can help to solve completion and acceptance challenges, they are not sufficient to ensure access to PADs in crises. To ensure accessibility, we propose digital PADs, which offer considerable potential for overcoming these aforementioned barriers. Embedded in national health data infrastructures, PADs could be completed and accessed by service users themselves, possibly with the support of facilitators, and retrieved by any clinic in an emergency. We highlight the strengths and limitations of digital PADs and point out that the proposed solutions must be developed collaboratively and take into account digital inequalities to be effective support for people with serious mental health conditions.

Digital interventions hold significant potential for improving physical activity (PA) and reducing sedentary behavior (SB) in adults. Despite increasing interest, there remain surprising gaps in the current knowledge of how best to deliver these interventions, including incorporating appropriate theoretical frameworks and behavior change techniques. Following numerous systematic reviews, there is now significant potential for umbrella reviews to provide an overview of the current evidence.

In Chinese traditional culture, discussions surrounding death are often considered taboo, leading to a poor quality of death, and limited public awareness and knowledge about palliative and end-of-life care (PEoLC). However, the increasing prevalence of social media in health communication in China presents an opportunity to promote and educate the public about PEoLC through online discussions.