ArticleClinical anatomy (New York, N.Y.)2026
Performance of Seven Large Language Models on Anatomy Examination Questions.
Article in Clinical anatomy (New York, N.Y.), 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.
- Accuracy of ChatGPT-4o in Identifying Anatomical Structures on Cadaveric Images: A Practical Anatomy Examination Study.Clinical anatomy (New York, N.Y.) · 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
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence is among the most rapidly developing branches of technology. It has proven to be a helpful tool in various fields, including medicine. Significant advances in the development of new language models prompt an evaluation of their effectiveness across various areas of medicine, including anatomy. This study aimed to assess the effectiveness of artificial intelligence in solving theoretical anatomy exams designed for medical students. The study utilized 555 multiple-choice questions (150 in Polish and 405 in English) sourced from past anatomy exams for the medical program. The models tested included: ChatGPT-4o mini, ChatGPT-4o, DeepSeek, Copilot, Gemini, and two Polish models: Bielik and PLLum. Each question was asked only once. For analysis purposes, the questions were categorized by type and by the anatomical structure they addressed. Out of 555 questions, ChatGPT-4o mini answered 394 correctly (71%), ChatGPT-4o - 461 (83.1%), DeepSeek - 427 (76.9%), Copilot - 442 (79.6%), Gemini - 439 (78.8%), Bielik - 166 (29.9%), and PLLum - 222 (40.0%). The language models performed poorest on multiple-answer questions (37.6%) and best on questions concerning the function of a given organ (75%). Most of the tested language models are capable of independently passing the exam, which should serve as a warning to teaching staff supervising students during exams and assessments. Properly formulated questions can currently hinder students relying on artificial intelligence from passing, but ongoing AI advancements may result in even higher pass rates in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.