  Skin and subcutaneous diseases rank high among the leading contributors to
the global burden of nonfatal diseases, impacting a considerable portion of the
population. Nonetheless, the field of dermatology diagnosis faces three
significant hurdles. Firstly, there is a shortage of dermatologists accessible
to diagnose patients, particularly in rural regions. Secondly, accurately
interpreting skin disease images poses a considerable challenge. Lastly,
generating patient-friendly diagnostic reports is usually a time-consuming and
labor-intensive task for dermatologists. To tackle these challenges, we present
SkinGPT-4, which is the world's first interactive dermatology diagnostic system
powered by an advanced visual large language model. SkinGPT-4 leverages a
fine-tuned version of MiniGPT-4, trained on an extensive collection of skin
disease images (comprising 52,929 publicly available and proprietary images)
along with clinical concepts and doctors' notes. We designed a two-step
training process to allow SkinGPT to express medical features in skin disease
images with natural language and make accurate diagnoses of the types of skin
diseases. 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 in search of a dependable and
precise diagnosis of their skin ailments. To demonstrate the robustness of
SkinGPT-4, we conducted quantitative evaluations on 150 real-life cases, which
were independently reviewed by certified dermatologists, and showed that
SkinGPT-4 could provide accurate diagnoses of skin diseases.
