Published on in Vol 23, No 9 (2021): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27122, first published .
Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study

Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study

Radiation Oncologists’ Perceptions of Adopting an Artificial Intelligence–Assisted Contouring Technology: Model Development and Questionnaire Study

Journals

  1. Chen M, Zhang B, Cai Z, Seery S, Gonzalez M, Ali N, Ren R, Qiao Y, Xue P, Jiang Y. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Frontiers in Medicine 2022;9 View
  2. Li C, Li Y. Factors Influencing Public Risk Perception of Emerging Technologies: A Meta-Analysis. Sustainability 2023;15(5):3939 View
  3. Huang S, Cheng Z, Lai L, Zheng W, He M, Li J, Zeng T, Huang X, Yang X. Integrating multiple MRI sequences for pelvic organs segmentation via the attention mechanism. Medical Physics 2021;48(12):7930 View
  4. Calisto F, Nunes N, Nascimento J. Modeling adoption of intelligent agents in medical imaging. International Journal of Human-Computer Studies 2022;168:102922 View
  5. Calisto F, Nunes N, Nascimento J. Modeling Adoption of Intelligent Agents in Medical Imaging. SSRN Electronic Journal 2022 View
  6. Hameed B, Naik N, Ibrahim S, Tatkar N, Shah M, Prasad D, Hegde P, Chlosta P, Rai B, Somani B. Breaking Barriers: Unveiling Factors Influencing the Adoption of Artificial Intelligence by Healthcare Providers. Big Data and Cognitive Computing 2023;7(2):105 View
  7. Lambert S, Madi M, Sopka S, Lenes A, Stange H, Buszello C, Stephan A. An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. npj Digital Medicine 2023;6(1) View
  8. Chen Y, Wu Z, Wang P, Xie L, Yan M, Jiang M, Yang Z, Zheng J, Zhang J, Zhu J. Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study. Journal of Medical Internet Research 2023;25:e48249 View
  9. Eiskjær S, Pedersen C, Skov S, Andersen M. Usability and performance expectancy govern spine surgeons’ use of a clinical decision support system for shared decision-making on the choice of treatment of common lumbar degenerative disorders. Frontiers in Digital Health 2023;5 View
  10. Hua D, Petrina N, Young N, Cho J, Poon S. Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review. Artificial Intelligence in Medicine 2024;147:102698 View
  11. Kleine A, Kokje E, Lermer E, Gaube S. Attitudes Toward the Adoption of 2 Artificial Intelligence–Enabled Mental Health Tools Among Prospective Psychotherapists: Cross-sectional Study. JMIR Human Factors 2023;10:e46859 View
  12. Alanzi T, Alotaibi R, Alajmi R, Bukhamsin Z, Fadaq K, AlGhamdi N, Bu Khamsin N, Alzahrani L, Abdullah R, Alsayer R, Al Muarfaj A, Alanzi N. Barriers and Facilitators of Artificial Intelligence in Family Medicine: An Empirical Study With Physicians in Saudi Arabia. Cureus 2023 View
  13. Shanbhag N, Bin Sumaida A, Binz T, Hasnain S, El-Koha O, Al Kaabi K, Saleh M, Al Qawasmeh K, Balaraj K. Integrating Artificial Intelligence Into Radiation Oncology: Can Humans Spot AI?. Cureus 2023 View
  14. Das S, Datta B. Application of UTAUT2 on Adopting Artificial Intelligence Powered Lead Management System (AI-LMS) in passenger car sales. Technological Forecasting and Social Change 2024;201:123241 View
  15. Weber S, Wyszynski M, Godefroid M, Plattfaut R, Niehaves B. How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey. Computational and Structural Biotechnology Journal 2024;24:146 View
  16. Dingel J, Kleine A, Cecil J, Sigl A, Lermer E, Gaube S. Predictors of Health Care Practitioners’ Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology. Journal of Medical Internet Research 2024;26:e57224 View
  17. Krieger J, Bouder F, Wibral M, Almeida R. A systematic literature review on risk perception of Artificial Narrow Intelligence. Journal of Risk Research 2024:1 View
  18. Botha N, Ansah E, Segbedzi C, Dumahasi V, Maneen S, Kodom R, Tsedze I, Akoto L, Atsu F. Artificial intelligent tools: evidence-mapping on the perceived positive effects on patient-care and confidentiality. BMC Digital Health 2024;2(1) View
  19. Perivolaris A, Adams-McGavin C, Madan Y, Kishibe T, Antoniou T, Mamdani M, Jung J. Quality of interaction between clinicians and artificial intelligence systems. A systematic review. Future Healthcare Journal 2024;11(3):100172 View
  20. Lee J, Cho W, Kim B, Yoon D, Kim J, Song J, Yang S, Lim S, Chung G, Choi J, Han Y, Kong H, Lee J, Kim S, Bae J. Impact of User’s Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection. Gut and Liver 2024;18(5):857 View
  21. Han Z, Wang Y, Wang W, Zhang T, Wang J, Ma X, Men K, Shi A, Gao Y, Bi N. Artificial intelligence-assisted delineation for postoperative radiotherapy in patients with lung cancer: a prospective, multi-center, cohort study. Frontiers in Oncology 2024;14 View
  22. Botha N, Segbedzi C, Dumahasi V, Maneen S, Kodom R, Tsedze I, Akoto L, Atsu F, Lasim O, Ansah E. Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety. Archives of Public Health 2024;82(1) View
  23. Tao W, Yang J, Qu X. Utilization of, Perceptions on, and Intention to Use AI Chatbots Among Medical Students in China: National Cross-Sectional Study. JMIR Medical Education 2024;10:e57132 View