[2605.04098] Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.04098 (cs)
[Submitted on 1 May 2026]
Title:Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology
Authors:Roy Jiang, Hyunjae Kim, Zhenyue Qin, Morten Lee, Margaret MacGibeny, Ailish Hanly, Angela Sadlowski, Shanin Chowdhury, Xuguang Ai, Jeffrey Gehlhausen, Qingyu Chen
View a PDF of the paper titled Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology, by Roy Jiang and 10 other authors
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Abstract:Multimodal large language models (MLLMs) have demonstrated promise on publicly available dermatology benchmarks. However, benchmark performance may not generalize to real-world dermatologic decision-making. To quantify this benchmark-to-bedside gap, we evaluated four open-weight MLLMs (InternVL-Chat v1.5, LLaVA-Med v1.5, SkinGPT4 and MedGemma-4B-Instruct) and one commercial MLLM (GPT-4.1) across three publicly available dermatology datasets and a retrospective multi-site hospital-based dermatology consultation cohort comprising 5,811 cases and 46,405 clinical images. Models were evaluated on two clinically relevant tasks: differential diagnosis generation and severity-based triage. Diagnostic performance was modest on public datasets and declined substantially in the real-world cohort. On public benchmarks, top-3 diagnostic accuracy reached 26.55% for the best open-weight model and 42.25% for GPT-4.1. On real-world consultation cases using images alone, top-3 diagnostic accuracy fell to 1.50%-13.35% among open-weight models and 24.65% for GPT-4.1. Incorporating clinical context improved performance across all models, increasing top-3 diagnostic accuracy up to 28.75% among open-weight models and 38.93% for GPT-4.1. However, model outputs were highly sensitive to incomplete or erroneous consultation context. For severity-based triage, models achieved moderate sensitivity (above 60%), suggesting potential utility for screening but insufficient reliability for clinical deployment. These findings demonstrate that benchmark performance substantially overestimates the real-world clinical capability of current dermatology MLLMs.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2605.04098 [cs.CV]
(or arXiv:2605.04098v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.04098
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arXiv-issued DOI via DataCite
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From: Ruoyi Jiang [view email] [v1] Fri, 1 May 2026 02:54:07 UTC (2,391 KB)
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