Skin diseases exhibit a wide range of variations,posing challenges in clinical diagnosis and differential diagnosis due to limited availability of auxiliary diagnostic tools.However,convolutional neural network(CNN),an artificial intelligence approach based on deep learning,have demonstrated comparable performance levels to dermatologists in the diagnosis and differentiation of skin diseases.This holds particularly true for conditions such as malignant skin tumors,pigmentary skin disorders,inflammatory skin diseases,and infectious skin diseases where CNN show significant potential and promise.This paper provides an overview of the fundamental principles and structure of CNN,commonly used models,their application in diagnosing skin diseases,as well as their advantages and limitations.In the future,artificial intelligence is expected to assist clinicians further by addressing healthcare access issues in regions with underdeveloped medical infrastructure while offering home care services for patients with chronic skin conditions and enabling automatic tracking and monitoring of such conditions.
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