ArticleJournal of cutaneous medicine and surgery
Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.
Article in Journal of cutaneous medicine and surgery. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparing Diagnostic Accuracy of Clinical Professionals and Large Language Models: Systematic Review and Meta-Analysis.JMIR medical informatics · 2025Pooled it
- Diagnostic Accuracy of Multimodal Large Language Models (LLMs) for Detecting Tuberous Breast Deformity from Standardized Photographs: A Descriptive in Silico Study.Aesthetic plastic surgery · 2026Article
- Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay people.Nature medicine · 2026Article
- A Comparative Assessment of ChatGPT, Gemini, and DeepSeek Accuracy: Examining Visual Medical Assessment in Internal Medicine Cases with and Without Clinical Context.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluating GPT-5 for Melanoma Detection Using Dermoscopic Images.Diagnostics (Basel, Switzerland) · 2025Article
- The Role of ChatGPT in Dermatology Diagnostics.Diagnostics (Basel, Switzerland) · 2025Review
- Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review.Journal of clinical medicine · 2024Review
- Neural Mosaics: Detecting Aberrant Brain Interactions using Algebraic Topology and Generative Artificial Intelligence.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Capabilities of GPT-4o and Gemini 1.5 Pro in Gram stain and bacterial shape identification.Future microbiology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe integration of generative artificial intelligence within dermatology presents a new frontier for enhancing diagnostic accuracy and treatment planning.
objectiveThis 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.
methodsA 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.
resultsIn the Image Prompt setup, GPT-4V correctly identified the primary diagnosis for 54% of the images. The Scenario Prompt and the Image + Scenario Prompt setups, respectively, both achieved an 89% accuracy rate in identifying the primary diagnosis. Treatment recommendations were evaluated using a modified Entrustment Scale, showing competent but not expert-level performance. A Wilcoxon signed-rank test demonstrated a statistically significant difference in treatment recommendations based on the Entrustment Score, with the model performing better in the Image + Scenario setup (
conclusionGPT-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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.