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Large language models for dermatological image interpretation - a comparative study.

Lasse Cirkel^1,2, Fabian Lechner^1,2, Lukas Alexander Henk^2,3

¹Institute of Artificial Intelligence, University Hospital Gießen-Marburg, Philipps University, Marburg, Germany.

Diagnosis (Berlin, Germany)

|May 27, 2025

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This summary is machine-generated.

Large language models (LLMs) show potential for diagnosing common skin conditions from images, with GPT-4o achieving the highest accuracy. However, performance varies, highlighting the need for cautious use and further development for reliable clinical decision support.

Area of Science:

Artificial Intelligence in Dermatology
Medical Image Analysis
Clinical Decision Support Systems

Background:

Interpreting dermatological findings presents challenges for both the public and medical professionals.
Large language models (LLMs) offer potential for accessible diagnostic support, but their efficacy in dermatology is not well-established.

Purpose of the Study:

To evaluate the diagnostic performance of various multimodal LLMs using dermatological images.
To compare the accuracy of different LLMs in identifying common skin diseases.

Main Methods:

Fifty0 dermatological images featuring psoriasis, vitiligo, erysipelas, and rosacea were analyzed.
Seven multimodal LLMs, including GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet, were tested using a standardized prompt for top diagnosis generation.

Keywords:

ChatGPT artificial intelligence dermatology diagnosis large language models skin pathology

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Main Results:

GPT-4o achieved the highest overall accuracy (67.8%), followed by GPT-4o mini (63.8%) and Llama3.2 11B (61.4%).
Diagnostic accuracy differed across conditions, with psoriasis showing the highest mean LLM accuracy (59.2%) and erysipelas the lowest (33.4%).
Clear disease features improved LLM accuracy, while Llama3.2 90B declined to diagnose images of intimate areas.

Conclusions:

LLM performance in dermatological image diagnosis is variable, necessitating careful application.
A free, locally deployable LLM achieved approximately 66% accuracy, indicating potential for secure, accessible clinical tools.
Improving LLM accuracy and integrating clinical data can enhance diagnostic support systems.