Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensiv...Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensive training datasets.The public release and rapid refinement of large language models(LLMs)like ChatGPT have accelerated the adoption of GAI across various medical specialties,offering new tools for education,clinical simulation,and research.Dermatology training,which heavily relies on visual pattern recognition and requires extensive exposure to diverse morphological presentations,faces persistent challenges such as uneven distribu-tion of educational resources,limited patient exposure for rare conditions,and variability in teaching quality.Exploring the integration of GAI into pedagogical frameworks offers innovative approaches to address these challenges,potentially enhancing the quality,standardization,scalability,and accessibility of dermatology ed-ucation.This comprehensive review examines the core concepts and technical foundations of GAI,highlights its specific applications within dermatology teaching and learning—including simulated case generation,per-sonalized learning pathways,and academic support—and discusses the current limitations,practical challenges,and ethical considerations surrounding its use.The aim is to provide a balanced perspective on the significant potential of GAI for transforming dermatology education and to offer evidence-based insights to guide future exploration,implementation,and policy development.展开更多
文摘Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensive training datasets.The public release and rapid refinement of large language models(LLMs)like ChatGPT have accelerated the adoption of GAI across various medical specialties,offering new tools for education,clinical simulation,and research.Dermatology training,which heavily relies on visual pattern recognition and requires extensive exposure to diverse morphological presentations,faces persistent challenges such as uneven distribu-tion of educational resources,limited patient exposure for rare conditions,and variability in teaching quality.Exploring the integration of GAI into pedagogical frameworks offers innovative approaches to address these challenges,potentially enhancing the quality,standardization,scalability,and accessibility of dermatology ed-ucation.This comprehensive review examines the core concepts and technical foundations of GAI,highlights its specific applications within dermatology teaching and learning—including simulated case generation,per-sonalized learning pathways,and academic support—and discusses the current limitations,practical challenges,and ethical considerations surrounding its use.The aim is to provide a balanced perspective on the significant potential of GAI for transforming dermatology education and to offer evidence-based insights to guide future exploration,implementation,and policy development.
