The rapid development of multimodal large language models (LLMs) such as GPT and Med-PaLM is reshaping medical practice. Skin cancer, one of the most prevalent malignancies, encompasses diverse subtypes and early signs that often resemble benign lesions, making timely detection and accurate diagnosis challenging. Traditional diagnostic methods are hindered by subjectivity, sampling bias, and low efficiency. Skin cancer diagnosis is largely constrained by strong subjectivity, sampling bias, and low diagnostic efficiency regularly. Although immunotherapy and targeted therapy aimed at the tumor microenvironment have brought new therapeutic possibilities to patients, tumor heterogeneity and immune evasion remain major unresolved challenges. Artificial intelligence techniques based on deep learning and complex neural networks can integrate dermoscopic images, histopathological information, and genetic databases through multimodal fusion strategies, enabling the extraction of richer and complementary features and thereby significantly improving diagnostic accuracy and robustness. Moreover, tailoring treatment strategies according to individual patient characteristics facilitates truly personalized therapy and prognostic assessment. In the field of drug development, artificial intelligence accelerates the screening and simulation of candidate compounds, substantially reducing development time and expenditure. This review summarizes recent advances in AI for skin cancer, with emphasis on early detection, individualized therapy, and patient management. We further discuss challenges related to data quality and model interpretability, emphasizing the importance of dermatology-specific foundation models and collaboration between clinicians and engineers.
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