ArticleFrontiers in medicine2026
Performance stability despite iteration: evaluating DeepSeek and ChatGPT on Chinese medical licensing examinations.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Comprehensive Evaluation of Large Language Models on Four Core Medical School Courses: A Cross-Sectional Comparative Study.Advances in medical education and practice · 2026Article
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3 authors.
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Abstract
Background: Large Language Models (LLMs) hold substantial potential in medical education. In our previous work, we evaluated the performance of DeepSeek-R1 and ChatGPT-4o on the Chinese National Medical Licensing Examination (CNMLE). Following performance upgrades in December 2025, DeepSeek-V3.2 and ChatGPT-5.2 were released. This study aimed to longitudinally assess the performance evolution of DeepSeek (R1 vs. V3.2) and ChatGPT (4o vs. 5.2) using the 2024 CNMLE as a baseline and to explore their performance on the 2025 CNMLE. Methods: We tested DeepSeek-V3.2 and ChatGPT-5.2 on 600 multiple-choice questions from the written part of the 2024 CNMLE, and compared the results with historical data from DeepSeek-R1 and ChatGPT-4o. The questions consisted of 4 units and were divided into low-difficulty and high-difficulty groups according to different difficulty levels. Additionally, the two latest LLMs were assessed on 600 questions from the written part of the 2025 CNMLE. Results: In the 2024 CNMLE, overall accuracy for the DeepSeek series (R1 vs. V3.2: 92.0% vs. 91.0%) and ChatGPT series (4o vs. 5.2: 87.2% vs. 89.3%) showed no significant differences (all Conclusion: Benchmarked against the 2024 CNMLE, iterative updates did not yield significant performance gains for either LLM series. However, DeepSeek-V3.2 demonstrated a performance advantage on the 2025 CNMLE.
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