From Diagnosis to Treatment: A Review of AI Applications in Psoriasis Management

Psoriasis, a chronic inflammatory skin condition affecting millions globally, is challenging to diagnose and manage due to reliance on visual inspection and subjective assessments. This warrants the need for objective, data-driven methods. Emerging artificial intelligence (AI) technologies offer promising solutions across various aspects of psoriasis care. This review explores the prevalence and challenges of psoriasis, the limitations of traditional diagnostic methods, and the applications of AI in psoriasis identification, classification, lesion segmentation, and personalized treatment planning. It provides a critical analysis of recent advancements, highlights unaddressed gaps, and outlines future research directions to enhance the role of AI in psoriasis management. A comprehensive literature search was conducted using targeted keywords on open-access databases, focusing on studies published in English from 2015 to 2024. Relevant original research on AI applications in psoriasis management, including diagnosis, classification, lesion segmentation, and treatment recommendations, was critically analyzed to identify advancements, gaps, and opportunities for improving patient outcomes. Findings of this review indicate that AI technologies enhanced diagnostic accuracy and treatment personalization. However, significant gaps remain in their integration into clinical practice and assessments of long-term efficacy. In contrast to previous review articles, this work covered a wider range of recent studies, identified critical gaps that have not been adequately addressed, and outlined future research directions for enhancing the role of AI in improving psoriasis management. This work serves as a valuable resource for researchers, clinicians, and technology developers, emphasizing the need for further exploration of the potential of AI to improve patient outcomes in psoriasis.