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

Viet Anh Nguyen, Thi Quynh Trang Vuong and Nguyen Van Hung

PLOS ONE, 2025, vol. 20, issue 10, 1-13

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

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335775

DOI: 10.1371/journal.pone.0335775

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