Objective: This systematic review evaluates the current applications, advantages, and challenges of large language models (LLMs) in melanoma care. Methods: A systematic search was conducted in PubMed and Scopus databases for studies published up to 23 July 2024, focusing on the application of LLMs in melanoma. The review adhered to PRISMA guidelines, and the risk of bias was assessed using the modified QUADAS-2 tool. Results: Nine studies were included, categorized into subgroups: patient education, diagnosis, and clinical management. In patient education, LLMs demonstrated high accuracy, though readability often exceeded recommended levels. For diagnosis, multimodal LLMs like GPT-4V showed capabilities in distinguishing melanoma from benign lesions, but accuracy varied, influenced by factors such as image quality and integration of clinical context. Regarding management advice, ChatGPT provided more reliable recommendations compared to other LLMs, but all models lacked depth for individualized decision-making. Conclusions: LLMs, particularly multimodal models, show potential in improving melanoma care. However, current applications require further refinement and validation. Future studies should explore fine-tuning these models on large, diverse dermatological databases and incorporate expert knowledge to address limitations such as generalizability across different populations and skin types.

Background: Large language models (LLMs) are gaining recognition across various medical fields; however, their specific role in dermatology, particularly in melanoma care, is not well- defined. This systematic review evaluates the current applications, advantages, and challenges associated with the use of LLMs in melanoma care. Methods: We conducted a systematic search of PubMed and Scopus databases for studies published up to July 23, 2024, focusing on the application of LLMs in melanoma. Identified studies were categorized into three subgroups: patient education, diagnosis and clinical management. The review process adhered to PRISMA guidelines, and the risk of bias was assessed using the modified QUADAS-2 tool. Results: Nine studies met the inclusion criteria. Five studies compared various LLM models, while four focused on ChatGPT. Three studies specifically examined multi-modal LLMs. In the realm of patient education, ChatGPT demonstrated high accuracy, though it often surpassed the recommended readability levels for patient comprehension. In diagnosis applications, multi- modal LLMs like GPT-4V showed capabilities in distinguishing melanoma from benign lesions. However, the diagnostic accuracy varied considerably, influenced by factors such as the quality and diversity of training data, image resolution, and the models’ ability to integrate clinical context. Regarding management advice, one study found that ChatGPT provided more reliable management advice compared to other LLMs, yet all models lacked depth and specificity for individualized decision-making. Conclusions: LLMs, particularly multimodal models, show potential in improving melanoma care through patient education, diagnosis, and management advice. However, current LLM applications require further refinement and validation to confirm their clinical utility. Future studies should explore fine-tuning these models on large dermatological databases and incorporate expert knowledge.