Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of...Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of personalization,and inadequate assessment of competency development.Recent advances in artificial intelligence(AI)offer new technological support for dermatology education.To address the risk of fragmented adoption,a process-oriented approach,conceptualizing AI-assisted dermatology education as an integrated system embedded throughout the learning process is adopted.Within this framework,AI is not only examined as an isolated tool but also as a component aligned with educational workflows.AI′s primary applications in dermatology education are analyzed,focusing on its potential to improve standardization so as to expand access to high-quality resources and support competency-based teaching,and facilitate lifelong learning.Meaningful educational benefits emerge when AI is systematically integrated into structured teaching processes.However,associated risks-including data bias,learner overreliance,implementation constraints,and potential impacts on medical humanities education must also be considered.Based on these findings,the strategic principles centered on educational objectives are proposed,emphasizing human-AI collaboration,transparency,and continuous governance to support the sustainable development of dermatology talent.展开更多
文摘Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of personalization,and inadequate assessment of competency development.Recent advances in artificial intelligence(AI)offer new technological support for dermatology education.To address the risk of fragmented adoption,a process-oriented approach,conceptualizing AI-assisted dermatology education as an integrated system embedded throughout the learning process is adopted.Within this framework,AI is not only examined as an isolated tool but also as a component aligned with educational workflows.AI′s primary applications in dermatology education are analyzed,focusing on its potential to improve standardization so as to expand access to high-quality resources and support competency-based teaching,and facilitate lifelong learning.Meaningful educational benefits emerge when AI is systematically integrated into structured teaching processes.However,associated risks-including data bias,learner overreliance,implementation constraints,and potential impacts on medical humanities education must also be considered.Based on these findings,the strategic principles centered on educational objectives are proposed,emphasizing human-AI collaboration,transparency,and continuous governance to support the sustainable development of dermatology talent.
