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Currently submitted to: JMIR mHealth and uHealth

Date Submitted: May 6, 2026
Open Peer Review Period: May 8, 2026 - Jul 3, 2026
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Training Trajectories Reveal Cross-Modality Generalization Gaps in 12-Lead–Trained AI-ECG Deployed on Smartwatch Recordings

  • Hak Seung Lee; 
  • Jong-Hwan Jang; 
  • Sora Kang; 
  • Yong-Yeon Jo; 
  • Jeong Min Son; 
  • Min Sung Lee; 
  • Kyung Su Kim; 
  • Joon-myoung Kwon; 
  • Kyung-Hee Kim

ABSTRACT

AI-ECG models trained on 12-lead–derived Lead I demonstrated comparable endpoint discrimination on smartwatch-derived Lead I but substantially divergent training trajectories, revealing cross-modality generalization gaps not detected by endpoint metrics alone.


 Citation

Please cite as:

Lee HS, Jang JH, Kang S, Jo YY, Son JM, Lee MS, Kim KS, Kwon Jm, Kim KH

Training Trajectories Reveal Cross-Modality Generalization Gaps in 12-Lead–Trained AI-ECG Deployed on Smartwatch Recordings

JMIR Preprints. 06/05/2026:100524

DOI: 10.2196/preprints.100524

URL: https://preprints.jmir.org/preprint/100524

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