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

Artificial Intelligence (4682) Cardiology (282) Imaging Informatics (456) Cardiac Arrhythmia, Atrial Fibrillation (141) Generative Language Models Including ChatGPT (1477) AI Language Models in Health Care (733) Applications of AI (923)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/86692, first published .
Medical professionals review ECG data on computer screens, discussing patient care.

Performance of Vision-Enabled Large Language Models in Image-Based Electrocardiogram Interpretation: Exploratory Evaluation

Performance of Vision-Enabled Large Language Models in Image-Based Electrocardiogram Interpretation: Exploratory Evaluation

Authors of this article:

Nibras Soubh1, 2 Author Orcid Image ;   Eva Rasenack1, 2 Author Orcid Image ;   Helge Haarmann1, 2 Author Orcid Image ;   Felix Wiedmann1, 2 Author Orcid Image ;   Markus Zabel1, 2 Author Orcid Image ;   Constanze Schmidt1, 2 Author Orcid Image ;   Rayan Suliman3 Author Orcid Image ;   Leonard Bergau1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations (1) Metrics

Journals

  1. Chai G, Chen S, Yang J, Chen X, Zhang Q, Kutryk M. Performance of large language models in electrocardiogram interpretation: A comparative study. Journal of Electrocardiology 2026;98:154411 View