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This paper is in the following e-collection/theme issue:

Ethical, Legal, and Social Issues in AI (217)

Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47449, first published .
Doctor and patient reviewing medical data on a computer screen

Physicians’ and Machine Learning Researchers’ Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study

Physicians’ and Machine Learning Researchers’ Perspectives on Ethical Issues in the Early Development of Clinical Machine Learning Tools: Qualitative Interview Study

Authors of this article:

Jane Paik Kim1 Author Orcid Image ;   Katie Ryan1 Author Orcid Image ;   Max Kasun1 Author Orcid Image ;   Justin Hogg1 Author Orcid Image ;   Laura B Dunn2 Author Orcid Image ;   Laura Weiss Roberts1 Author Orcid Image
Article Authors Cited by (15) Tweetations (2) Metrics

Journals

  1. Kim J, Yang H, Kim B, Ryan K, Roberts L. Understanding Physician’s Perspectives on AI in Health Care: Protocol for a Sequential Multiple Assignment Randomized Vignette Study. JMIR Research Protocols 2024;13:e54787 View
  2. Roberts L. Addressing the Novel Implications of Generative AI for Academic Publishing, Education, and Research. Academic Medicine 2024;99(5):471 View
  3. Chen H, Alfred M, Brown A, Atinga A, Cohen E. Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study. JMIR Formative Research 2024;8:e59045 View
  4. Kim B, Ryan K, Kim J. Assessing the impact of information on patient attitudes toward artificial intelligence-based clinical decision support (AI/CDS): a pilot web-based SMART vignette study. Journal of Medical Ethics 2025;51(8):541 View
  5. Dexter R, Kostick-Quenet K, Blumenthal-Barby J. A multi-site study of clinician perspectives in the lifecycle of an algorithmic risk prediction tool. SSM - Qualitative Research in Health 2025;7:100562 View
  6. Ryan K, Yang H, Kim B, Kim J. Assessing the impact of AI on physician decision-making for mental health treatment in primary care. npj Mental Health Research 2025;4(1) View
  7. Kim B, Ryan K, Kasun M, Roberts L, Kim J. Online survey assessing US primary care physicians’ attitudes toward AI use in clinical administrative tasks. BMJ Health & Care Informatics 2026;33(1):e101400 View
  8. Fantus S, Li J, Wang T, Tang L. Ethical Knowledge, Challenges, and Institutional Strategies Among Medical AI Developers and Researchers: Focus Group Study. Journal of Medical Internet Research 2026;28:e79613 View
  9. Schilder M, Keyser A, van Hees S, Sbrizzi A, Boon W. Anticipating Moral and Economic Considerations, Opportunities, and Potential Frictions for AI in Medical Imaging: Multistakeholder Cocreation Study. Journal of Medical Internet Research 2026;28:e83407 View
  10. Jiang Q, Xu J, Li L, Ye L, Lin L, Wu J, Wu Y. Moving toward upstream governance: a Phase 1 Ethical Review framework for clinical machine learning. AI and Ethics 2026;6(2) View
  11. Zhang Y, Liu Y, Lv T, Wang J, Liu H, Li X. Evaluating ChatGPT's Adherence to Medical Ethics: A Prerequisite for Artificial Intelligence in Medicine. Health Care Science 2026;5(2):98 View
  12. Domínguez-García A, Moguel-Cardín W, Cervantes-Arriaga A, Rodríguez-Violante M. Neurorights: safeguarding mental integrity in the face of neurotechnology. Acta Neurológica Colombiana 2026 View
  13. Vargas J, Hoffman H, Schirmer C, Hui F, Kono K, Booth T, Kellogg R, Levitt M. Part 1: Artificial intelligence in neurointerventional surgery: current state and challenges – a white paper. JNIS Advances 2026;1(1):e000011 View

Books/Policy Documents

  1. Xu J, Babaian T. Biomedical Engineering Systems and Technologies. View

Conference Proceedings

  1. Kuo K, Yadav C, Smith V. 2026 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). Position: Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice View