Advancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review
Payton Smith, Chandler Johnson, Kathryn Haran, Faye Orcales, Allison Kranyak, Tina Bhutani, Josep Riera‐Monroig, Wilson Liao
Abstract
Purpose of Review: Machine learning (ML), a subset of artificial intelligence (AI), has been vital in advancing tasks such as image classification and speech recognition. Its integration into clinical medicine, particularly dermatology, offers a significant leap in healthcare delivery. Recent Findings: This review examines the impact of ML on psoriasis-a condition heavily reliant on visual assessments for diagnosis and treatment. The review highlights five areas where ML is reshaping psoriasis care: diagnosis of psoriasis through clinical and dermoscopic images, skin severity quantification, psoriasis biomarker identification, precision medicine enhancement, and AI-driven education strategies. These advancements promise to improve patient outcomes, especially in regions lacking specialist care. However, the success of AI in dermatology hinges on dermatologists' oversight to ensure that ML's potential is fully realized in patient care, preserving the essential human element in medicine. Summary: This collaboration between AI and human expertise could define the future of dermatological treatments, making personalized care more accessible and precise.