2025/08/31 by Lian Duan, Ting Li, Bowei Li +5 · 1 voice
Computer Science · Medicine · #AI in cancer detection #Cutaneous Melanoma Detection and Management #Digital Imaging in Medicine
paper · doi:10.1016/j.imed.2025.08.004
openalex publication_date 2025/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
Computer vision (CV) and natural language processing (NLP) are two crucial subsets of artificial intelligence (AI). Large language models (LLMs) represent a significant application of deep learning (DL) in NLP. LLMs are AI systems with transformer architectures trained on large-scale datasets that are capable of understanding and generating natural language. LLMs have been widely applied in tasks such as text generation and translation. In dermatology, LLMs have been used for various purposes, such as diagnostic assistance, medical practice and decision support, patient communication, and professional education. However, dermatology differs from other medical specialties in that the diagnosis of diseases, selection of treatment methods, and prediction of prognoses rely heavily on the recognition of visual patterns. Therefore, in dermatology, LLMs must simultaneously process dermatological images and natural language. The aim of this study was to systematically review NLP and CV applications of LLMs in dermatology. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the applications of LLMs in dermatological NLP and image processing tasks were systematically reviewed. After searching the MEDLINE (PubMed), Web of Science, and Scopus databases, a total of 38 original studies were included. The results show that, in NLP tasks, LLMs have demonstrated satisfactory performance in some studies, but most image processing results are not ideal. This review summarizes the research findings on the applications of LLMs in dermatological natural language and image analysis, aiming to identify LLMs capable of simultaneously processing both text and images as a direction for future development.