ArticleJournal of medical systems2025
Towards A Fair Duel: Reflections on the Evaluation of DeepSeek-R1 and ChatGPT-4o in Chinese Medical Education.
Article in Journal of medical systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Evaluating the efficacy and readability of advanced large language models in responding to patients' frequently asked questions about chronic rhinosinusitis: a comparative analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Performance benchmarking of LLMs on Chinese national medical licensing education: Cross-lingual and question-type effects.PloS one · 2026Article
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Authors and funding
1 author.
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Abstract
The recent study by Wu et al. (2025) comparing DeepSeek-R1 and ChatGPT-4o on the Chinese National Medical Licensing Examination (CNMLE) provides an important contribution to understanding large language model (LLM) performance in non-English medical contexts. While their findings highlight the potential of LLMs in medical knowledge assessment, several methodological issues merit further discussion. First, the exclusive use of Chinese-language items without bilingual comparison may favor DeepSeek-R1, which demonstrates strong performance in Chinese, over ChatGPT-4o, whose training corpus is predominantly English-based. Second, the evaluation was conducted before the release of GPT-5, leading to potential disparities in reasoning capabilities between models. Third, the restriction to multiple-choice questions limits the assessment to factual recall rather than higher-order reasoning or clinical judgment. We commend the authors for initiating this valuable cross-linguistic analysis and suggest that future studies incorporate bilingual testing, ensure model functional parity, and include open-ended clinical items to more comprehensively evaluate LLMs' reasoning and interpretive competence in real-world medical education contexts.
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Registered trials
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