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

Viewpoints and Perspectives (1462) E-Health Policy and Health Systems Innovation (548) Theoretical Frameworks and Concepts (582) Artificial Intelligence (4978) Health Information Governance (129) Ethical, Legal, and Social Issues in AI (217) Responsible Health AI (254) AI Governance and Policy (77)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/90654, first published .
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Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight

Co-Lifecycle Governance for Learning Medical AI: A Hybrid Convergence Framework for Adaptive Regulatory Oversight

Authors of this article:

Jae Hyun Lee1 Author Orcid Image ;   Boram Choi1 Author Orcid Image ;   Kwunho Jeong1 Author Orcid Image ;   Sang Won Suh2 Author Orcid Image ;   Ju Han Kim3 Author Orcid Image ;   Dae-Soon Son4, 5, 6 Author Orcid Image
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Journals

  1. Xia X, Khan W, Khan Q, Zhong C, Khan M. Artificial intelligence in cardiovascular imaging and risk stratification: From algorithmic discovery to meaningful clinical implication. Trends in Cardiovascular Medicine 2026 View
  2. Gaikwad A. From chemometrics to foundation models in pharmaceutical analysis: Current evidence, limitations, and future directions. Current Pharmaceutical Analysis 2026;22(7):594 View
  3. Lee J, Choi B, Jeong K, Suh S, Kim J, Son D. Refined Exclusion in Medical AI: Data Justice and Patient Safety Governance. Journal of Medical Internet Research 2026;28:e102359 View
  4. Adam M, Axmed Osmaan A, Mohamed M, Cali M, Khalif I. Predicting pandemics, predicting people: the ethical limits of AI-powered public health surveillance. Frontiers in Public Health 2026;14 View