Critical evaluation of large language models for human cross-sectional anatomy identification: implications for collaborative intelligence

Abstract Objective Rapid advances in artificial intelligence have increased interest in using large language models (LLMs) for medical education and clinical applications. This exploratory study evaluated the ability of three multimodal LLMs, ChatGPT 5, Gemini 2.5 Flash, and Grok 4, to identify anatomical structures in cross-sectional images of the upper and lower limbs. Results Twenty cross-sectional images, each highlighting a single anatomical structure, were presented to the models with standardized prompts specifying the anatomical region. Accuracy was scored for each model. ChatGPT 5 correctly identified 9 of 20 structures (45%, 95% CI: 23.1–68.5%), Gemini 2.5 Flash 5 of 20 (25%, 95% CI: 8.7–49.1%), and Grok 4 4 of 20 (20%, 95% CI: 5.7–43.7%). A qualitative error analysis revealed common misclassification patterns. These results indicate modest accuracy under the tested conditions and highlight areas for model improvement.