Exploring the role of Large Language Models in Melanoma: a Systemic Review
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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.
