Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4

Abstract

Abstract Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently. However, it is important to note that most current LLMs are limited to text interaction alone. Meanwhile, the development of multimodal large language models for medical diagnosis is still in its early stages, particularly considering the prevalence of image-based data in the field of medical diagnosis, among which dermatological diagnosis is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Inspired by current state-of-the-art multimodal large language models, we present SkinGPT-4, which is the world’s first interactive dermatology diagnostic system based on multimodal large language models. To implement SkinGPT-4, we have designed a new framework that aligned a pre-trained vision transformer with a large language model named Falcon-40B-Instruct, which is based on Falcon. To train SkinGPT-4, we have collected an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors’ notes and designed a two-step training strategy. To demonstrate the robustness of SkinGPT-4, we have conducted quantitative evaluations on 150 real-life cases, which were independently reviewed by certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identifies the characteristics and categories of the skin conditions, performs in-depth analysis, and provides interactive treatment recommendations. Meanwhile, SkinGPT-4’s local deployment capability and commitment to user privacy also render it an appealing choice for patients. Though SkinGPT-4 is not a substitute for doctors, it could enhance users’ comprehension of their medical conditions, facilitate improve communication between patients and doctors, expedite the diagnostic process for dermatologists, facilitate triage, and potentially promote human-centred care and healthcare equity in underdeveloped areas. In summary, SkinGPT-4 represents a significant leap forward in the field of dermatology diagnosis in the era of large language models and a valuable exploration of multimodal large language models in medical diagnosis.