ArticleNature communications2024
Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 3 of them syntheses that pooled it.
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Who cites it
75 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Revolutionizing dermatopathology using AI in skin diagnostics: scoping review.Frontiers in medicine · 2026Pooled it
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Multimodal Large Language Models in Dermatologic Diagnosis: A Game Changer or a Clinical Risk?Acta dermato-venereologica · 2026Article
- Comparative Expert Evaluation of Multimodal Large Language Models for Pediatric Rash Diagnosis: Clinical Utility, Safety, Information Quality, and Readability.Children (Basel, Switzerland) · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- The Evolving Role of Artificial Intelligence in Dermatology: A Meta-Analysis of Diagnostic Performance, Clinical Applications, and Implementation Challenges (2003-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Diagnostic Accuracy of Multimodal Large Language Models for Four-Class Benchmark of Oral Autoimmune Blistering Diseases: A Multicenter Paired Study.Diagnostics (Basel, Switzerland) · 2026Article
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- Evaluation of Large Language Models in Generating Physical Exercise Rehabilitation Programs for Musculoskeletal Disorders Across Multiple Clinical Scenarios.Healthcare (Basel, Switzerland) · 2026Article
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Review
- Memorization in large language models in medicine prevalence characteristics and implications.Nature communications · 2026Article
- A Multimodal Approach for Deep-Learning Classification of Vocal Fold Pathologies in Stroboscopy.The Laryngoscope · 2026Article
- MAP: evaluation and multi-agent enhancement of large language models for inpatient pathways.npj health systems · 2026Article
- From Pixel Understanding to Semantic Insight: Intelligent Detection in Sensor-Driven Perception Systems.Sensors (Basel, Switzerland) · 2026Review
- Artificial intelligence-enabled precision medicine for inflammatory skin diseases.The Journal of investigative dermatology · 2026Review
- ChatGPT, Gemini, and Claude in clinical and dermoscopic image analysis of basal cell carcinoma and its common mimickers: A comparative performance analysis.JID innovations : skin science from molecules to population health · 2026Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
15 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
Abstract
Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors' notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.
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Registered trials
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 Socratic graph.