ArticlePloS one2026
Performance of DeepSeek and ChatGPT on the Chinese Health Professional and Technical Examination: A comparative study.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Comment on "Humans vs. large language models in neurology board examination: performance, limitations, and reference reliability".Acta neurologica Belgica · 2026Article
- Evaluating the performance of DeepSeek-V3, Doubao, ERNIE 4.5 Turbo and iFLYTEK Spark in the Chinese national nursing licensing examination: a cross-sectional comparative study.Frontiers in artificial intelligence · 2026Article
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6 authors.
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Abstract
backgroundLarge language models (LLMs) are increasingly applied in medical education, yet their reliability in specialized, high-stakes assessments such as the Chinese Health Professional and Technical Examination remains unclear. DeepSeek-R1, a recently released reasoning-enhanced LLM, has shown promising performance, but empirical evidence within nursing examination contexts is limited.
objectiveTo compare the performance of DeepSeek-R1 and the GPT-4o API on the Chinese Health Professional and Technical Examination (Intermediate Nursing), focusing on accuracy, response consistency, and consistent accuracy.
methodsFour hundred official practice examination multiple-choice questions were categorized into four competency units and two question types (A/B). Both models were evaluated using overall accuracy, consistency (agreement across repeated responses), and consistent accuracy (proportion of responses that were both consistent and correct). Stratified analyses were performed across units, question types, and disciplines. Chi-square tests were used for statistical comparison, and Holm-Bonferroni correction was applied for multiple comparisons.
resultsDeepSeek-R1 demonstrated significantly higher overall accuracy than the GPT-4o API (88.5% vs. 67.9%, P < 0.001). GPT-4o API showed higher response consistency (96.5% vs. 88.5%) but lower consistent accuracy (66.7% vs. 84.0%). After multiple-comparison correction, significant differences in consistent accuracy remained in basic knowledge, professional knowledge, professional practice ability and Type A questions, as well as in surgical and gynecological nursing disciplines, while other domains showed no statistically significant differences.
conclusionDeepSeek-R1 outperformed the GPT-4o API across multiple dimensions of nursing competency assessment, particularly in overall accuracy and consistent accuracy. GPT-4o API exhibited high response stability but a tendency toward systematic errors, underscoring the need for careful interpretation of model outputs. Further research is needed to evaluate LLM performance using open-ended clinical reasoning tasks and real-world assessment data to support safe and effective educational integration.
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