Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study

Abstract

BACKGROUND: The integration of generative artificial intelligence within dermatology presents a new frontier for enhancing diagnostic accuracy and treatment planning. OBJECTIVE: This research evaluates Generative Pre-trained Transformer-4 Vision's (GPT-4V) performance in accurately diagnosing and generating treatment plans for common dermatological conditions, comparing its assessment of textual versus image data and its performance with multimodal inputs. METHODS: A dataset of 102 images representing 9 common dermatological conditions was compiled from dermatlas.org and dermnet.nz. Images were screened by 2 board-certified dermatologists and were excluded if they did not represent a classic presentation of the respective conditions. Fifty-four images were included in the final analysis. In addition, 9 text-based clinical scenarios corresponding to each condition were developed. GPT-4V's diagnostic capabilities were assessed across 3 setups: Image Prompt, Scenario Prompt, and Image + Scenario Prompt. RESULTS: < .01). CONCLUSION: GPT-4V demonstrates the potential to augment dermatological diagnosis and treatment recommendations, particularly in text-based scenarios. However, its underwhelming performance in image-based diagnosis and integration of multimodal data highlights important areas for improvement.

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