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

Design and Evaluation of Medical Education Materials (83) Testing and Assessment in Medical Education (235) Artificial Intelligence (4934) Artificial Intelligence (AI) in Medical Education (829) Generative Language Models Including ChatGPT (1600) Applications of AI (1056)

Published on in Vol 12 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77978, first published .
Three medical professionals in a meeting, reviewing documents.

Multiple Large Language Models’ Performance on the Chinese Medical Licensing Examination: Quantitative Comparative Study

Multiple Large Language Models’ Performance on the Chinese Medical Licensing Examination: Quantitative Comparative Study

Authors of this article:

Yanyu Diao1 Author Orcid Image ;   Mengyuan Wu1 Author Orcid Image ;   Jingwen Xu1 Author Orcid Image ;   Yifeng Pan1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Tang Y, Chen J, Wang S, Karobari M. Performance benchmarking of LLMs on Chinese national medical licensing education: Cross-lingual and question-type effects. PLOS One 2026;21(4):e0346518 View
  2. Zheng L, Lin Z, Liu X, Fan Z, He Z, Xu J, Yin L. Critical Care–Specific vs General-Purpose Large Language Models in Emergency Intensive Care Unit Diagnosis: Single-Center Retrospective Paired Comparative Study. Journal of Medical Internet Research 2026;28:e98026 View
  3. Zhang J, Wu L, Wu J, Han X, Lin L, Zhao N, Zhang Z, Cai M, Chen M, Yang S, Li B. CDTD-Agent: a retrieval-augmented multi-agent digital health framework for integrated diabetes care: a single-case expert-rated pilot study. Frontiers in Digital Health 2026;8 View