Work

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

Year: 2026

Type: article

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.

Source: BMC Research Notes

DOI: 10.1186/s13104-026-07634-8

Authors: Aryan Kermansaravi, Elenasadat Tonekabonipour, Mohamed Abdelhalim, Salman Farooq Dar, Bassem Amr

Institutions: Iran University of Medical Sciences, County Durham and Darlington NHS Foundation Trust

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,690