Diagnostic Accuracy of Commercial Large Language Models for Anogenital Skin Lesion Images: A Comparative Study of Gemini, Claude, and ChatGPT

作者：Nyi Nyi Soe, Phyu Mon Latt, David Lee, Ei T. Aung, Ryan Horn, Jason J. Ong, Christopher K Fairley, Eric P F Chow · 发表于：The Journal of Infectious Diseases · 年份：2026 · DOI：10.1093/infdis/jiag258 · 被引用次数：2 · 研究领域：Cutaneous Melanoma Detection and Management、Dermatological and COVID-19 studies、Dermatology and Skin Diseases

BACKGROUND: Diagnosing anogenital dermatological conditions often requires specialist expertise that is unavailable in many clinical settings. Large language models (LLMs) are increasingly accessible to clinicians, but their diagnostic accuracy for anogenital dermatology has not been evaluated. We evaluated the diagnostic accuracy of three LLMs (Gemini 2.5 Pro, Claude Opus 4.1, and ChatGPT 5 Thinking). METHODS: This study was conducted between September and November 2025, using de-identified clinical images of anogenital conditions from the STI Atlas (stiatlas.org (https://stiatlas.org/)) and other publicly available sources. Primary outcomes were correct classification of images identified as sexually transmitted infections (STIs) vs non-STIs and the inclusion of the correct diagnosis among the LLMs' top-ranked (top-1), top-3, or top-5 differential diagnoses. RESULTS: Among 218 images, Gemini achieved the highest accuracy for STI binary classification (76.2% [95% CI, 70.5% - 81.9%]) and differential diagnosis (top-1, 39.0% [95% CI, 32.7% - 45.7%]; top-3, 54.6% [95% CI, 47.9% - 61.1%]; top-5, 60.6% [95% CI, 53.9% - 66.9%]), followed by ChatGPT and Claude. In subgroup analysis, all LLMs showed substantially reduced accuracy for diagnostically challenging images (top-5 accuracy range, 29.2% - 40.0%). Gemini consistently outperformed Claude across most subgroups (P < 0.05). None of the LLMs could identify any mpox correctly. CONCLUSION: LLMs showed limited accuracy for diagnosin...