Benchmarking large-language-model vision capabilities in oral and maxillofacial anatomy: A cross-sectional study.

ABSTRACT:

Background

Multimodal large-language models (LLMs) have recently gained the ability to interpret images. However, their accuracy on anatomy tasks remains unclear.

Methods

A cross-sectional, atlas-based benchmark study was conducted in which six publicly accessible chat endpoints, including paired "deep-reasoning" and "low-latency" modes from OpenAI, Microsoft Copilot, and Google Gemini, identified 260 numbered landmarks on 26 high-resolution plates from a classical anatomic atlas. Each image was processed twice per model. Two blinded anatomy lecturers scored responses, including accuracy, run-to-run consistency, and per-label latency, which were compared with χ² and Kruskal-Wallis tests.

Results

Overall accuracy differed significantly among models (χ² = 73.2, P < 0.00

SUBMITTER: Nguyen VA 

PROVIDER: S-EPMC12561936 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature