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Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/90654, first published .
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

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

Jae Hyun Lee   1 , JD, DDS ;   Boram Choi   1 , DDS, PhD ;   Kwunho Jeong   1 , BS ;   Sang Won Suh   2 , MD, PhD ;   Ju Han Kim   3 , MD, PhD ;   Dae-Soon Son   4, 5, 6 , PhD

1 Global Research Center, JNPMEDI, Seoul, Republic of Korea

2 Department of Physiology, College of Medicine, Hallym University, Chuncheon, Gangwon-do, Republic of Korea

3 Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), College of Medicine, Seoul National University, Seoul, Republic of Korea

4 Major in Bio-Healthcare Convergence, College of Natural Sciences, Hallym University, Chuncheon, Gangwon-do, Republic of Korea

5 Division of Big Data and Artificial Intelligence, Institute of New Frontier Research, College of Medicine, Hallym University, Chuncheon, Gangwon-do, Republic of Korea

6 Hallym AI-BioHealth R&BD Center, Research Institute of Medical-Bio Convergence, Hallym University, Chuncheon, Gangwon-do, Republic of Korea

Corresponding Author:

  • Dae-Soon Son, PhD
  • Major in Bio-Healthcare Convergence
  • College of Natural Sciences, Hallym University
  • 1 Hallymdaehak-gil
  • Chuncheon, Gangwon-do 24252
  • Republic of Korea
  • Phone: 82 332482037
  • Email: biostat@hallym.ac.kr