aceBenchmarking Large Language Models (LLMs) Against Medical Students Using Psychometrically Evaluated Authentic Clinically Oriented Anatomy Multiple-Choice Questions (MCQs) at the Apply and Analyze Levels of Bloom's Taxonomy
Large language models (LLMs) are increasingly used by medical students as study aids, yet evidence regarding their performance on higher-order, clinically oriented anatomy questions relative to that of students-particularly at the Apply and Analyze levels of Bloom's Taxonomy-remains limited. This study aimed to compare the accuracy of five LLMs (ChatGPT-5, Gemini 2.5 Pro, Claude, Microsoft Copilot, and DeepSeek R1) with that of medical students on authentic, psychometrically evaluated anatomy multiple-choice que…
All five tested LLMs answered every one of 20 Apply- and Analyze-level anatomy questions correctly, while students averaged about two-thirds accuracy on the same items.
When LLMs rated each other's anatomy answers, they scored them as good as or better than human observers did, never lower, which undermines the reliability of LLM-based peer assessment.
The study used only 20 questions from a single institution, each given once to the models, and perfect LLM scores left zero variance, so the models could not be statistically compared.
Evidence
- Peer-reviewedClinical Anatomy2026-10-01
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Truvace Impact Record TRV-2026-1428, v1: “Benchmarking Large Language Models (LLMs) Against Medical Students Using Psychometrically Evaluated Authentic Clinically Oriented Anatomy Multiple-Choice Questions (MCQs) at the Apply and Analyze Levels of Bloom's Taxonomy.” Truvace, 2026-10-15. /record/TRV-2026-1428 (accessed at citation time). sha256 930044f903ecb4bc…
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