ArticleBMC research notes2026
Critical evaluation of large language models for human cross-sectional anatomy identification: implications for collaborative intelligence.
Article in BMC research notes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- 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
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveRapid 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.
resultsTwenty 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.
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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 Socratic graph.