Psoriasis is a chronic autoimmune skin condition characterized by a high incidence rate and a high recurrence rate. Early diagnosis and effective control are crucial for improving prognosis. Leveraging the robust capabilities of machine learning in image data processing, the creation of automated diagnostic and assessment tools for psoriasis has emerged as a prominent focus in artificial intelligence research within the field of dermatology in recent years. In order to strengthen dermatologists′ understanding of machine learning research in psoriasis, this paper mainly focuses on the application of machine learning in diagnosing, assessing, predicting progression, and forecasting treatment response in psoriasis. Additionally, it addresses the common issues in different areas utilizing machine learning technology and the prospective challenges of machine learning research for psoriasis. The aim is to provide reference for dermatologists, encouraging them to enhance collaboration with machine learning experts, engage in the advancement of psoriasis diagnosis, assessment, and prognostic models, and formulate multi-center prospective clinical studies to facilitate the integration of machine learning models into practical clinical settings.

Psoriasis; Machine learning; Deep learning; Convolutional neural networks