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

Artificial Intelligence (AI) in Medical Education (127) e-Learning and Digital Medical Education (803) Models and Frameworks in Medical Education (42) Machine Learning (1764) New Methods and Approaches in Medical Education (316) Development and Evaluation of Research Methods, Instruments and Tools (647) Testing and Assessment in Medical Education (72) Theme Issue: ChatGPT and Generative Language Models in Medical Education (102)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58898, first published .
Factors Associated With the Accuracy of Large Language Models in Basic Medical Science Examinations: Cross-Sectional Study

Factors Associated With the Accuracy of Large Language Models in Basic Medical Science Examinations: Cross-Sectional Study

Factors Associated With the Accuracy of Large Language Models in Basic Medical Science Examinations: Cross-Sectional Study

Authors of this article:

Naritsaret Kaewboonlert1 Author Orcid Image ;   Jiraphon Poontananggul1 Author Orcid Image ;   Natthipong Pongsuwan1 Author Orcid Image ;   Gun Bhakdisongkhram1 Author Orcid Image
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Journals

  1. Agarwal M, Sharma P, Wani P. Evaluating the Accuracy and Reliability of Large Language Models (ChatGPT, Claude, DeepSeek, Gemini, Grok, and Le Chat) in Answering Item-Analyzed Multiple-Choice Questions on Blood Physiology. Cureus 2025 View