ArticleScientific reports2025
Evolution of AI in anatomy education study based on comparison of current large language models against historical ChatGPT performance.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of ChatGPT and other large language models on multiple-choice questions in biomedical disciplines: A meta-analysis.Anatomical sciences education · 2026Pooled it
- Review
- Performance of Seven Large Language Models on Anatomy Examination Questions.Clinical anatomy (New York, N.Y.) · 2026Article
- Comparative evaluation of radiological anatomy knowledge and accuracy of ChatGPT-5, Gemini 2.5, and Grok 4 across normal and thinking modes.Anatomical sciences education · 2026Article
- Is artificial intelligence getting better at anatomy? A two-year review of ChatGPT's free public versions.Anatomical sciences education · 2026Article
- Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.Scientific reports · 2026Article
- Evaluating the clinical decision-making performance of large language models in clinically oriented thoracic anatomy scenarios: a comparative evaluation study.BMC medical education · 2026Article
- Comparative analysis of Chinese large language model performance on atrial fibrillation questions.BMC cardiovascular disorders · 2026Article
- Consistency over accuracy: run-to-run stability of contemporary large language models on Turkish curriculum-aligned theoretical anatomy multiple-choice questions.BMC medical education · 2026Article
- A Comparative Analysis of AI-Language Models' MCQ Performance versus Medical Students Across Different Pediatric Topics.Advances in medical education and practice · 2026Article
- Accuracy, Reliability, and Bloom's Taxonomy Performance of Seven Large Language Models on Microbiology Questions.Advances in medical education and practice · 2026Article
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2 authors.
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Abstract
The integration of Large Language Models (LLMs) in medical education has gained significant attention, particularly in their ability to handle complex medical knowledge assessments. However, a comprehensive evaluation of their performance in anatomical education remains limited. To evaluate the performance accuracy of current LLMs compared to previous versions in answering anatomical multiple-choice questions and assessing their reliability across different anatomical topics. We analyzed the performance of four LLMs (GPT-4o, Claude, Copilot, and Gemini) on 325 USMLE-style MCQs covering seven anatomical topics. Each model attempted the questions three times. Results were compared with the previous year's GPT-3.5 performance and random guessing. Statistical analysis included chi-square tests for performance differences. Current LLMs achieved an average accuracy of 76.8 ± 12.2%, significantly higher than GPT-3.5 (44.4 ± 8.5%) and random responses (19.4 ± 5.9%). GPT-4o demonstrated the highest accuracy (92.9 ± 2.5%), followed by Claude (76.7 ± 5.7%), Copilot (73.9 ± 11.9%), and Gemini (63.7 ± 6.5%). Performance varied significantly across anatomical topics, with Head & Neck (79.5%) and Abdomen (78.7%) showing the highest accuracy rates, while Upper Limb questions showed the lowest performance (72.9%). Only 29.5% of questions were answered correctly by all LLMs, and 2.5% were never answered correctly. Statistical analysis confirmed significant differences between models and across topics (χ
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