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Performance benchmarking of LLMs on Chinese national medical licensing education: Cross-lingual and question-type effects

Author

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  • Yuxia Tang
  • Jian Chen
  • Shouju Wang

Abstract

Background: The cross-lingual and question-type variations affecting large language models (LLMs) accuracy on the Chinese national medical licensing educations remain insufficiently explored. Methods: In this cross-sectional study (May 13–20, 2025), 396 educational questions (198 English–Chinese pairs) were extracted from the Chinese national medical licensing examination. ChatGPT-4o, ChatGPT-o3, Gemini-2.5-pro, Deepseek-V3, Deepseek-R1, and Doubao-1.5-pro were prompted to provide answers. Responses were compared against reference answers, and accuracy was computed for three question types: basic knowledge (Type A), case analysis (Type B), and integrative judgment (Type C). Results: Across all question types and languages, Doubao-1.5-pro achieved the highest accuracy at 92.0% ± 1.3%, whereas ChatGPT-4o had the lowest accuracy at 82.8% ± 3.7%. There was a significant main effect of question type (P = 0.0038) but no main effect of language (P = 0.56). Post hoc tests confirmed that Type A performance exceeded Types B and C (P

Suggested Citation

  • Yuxia Tang & Jian Chen & Shouju Wang, 2026. "Performance benchmarking of LLMs on Chinese national medical licensing education: Cross-lingual and question-type effects," PLOS ONE, Public Library of Science, vol. 21(4), pages 1-8, April.
    • Handle: RePEc:plo:pone00:0346518
    DOI: 10.1371/journal.pone.0346518
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