Artificial Intelligence in Dermatology: A Comprehensive Review of Approved Applications, Clinical Implementation, and Future Directions
William J. Nahm, Nayyab Sohail, Joshua Burshtein, Mohamad Goldust, Maria Tsoukas
Abstract
This comprehensive review examines artificial intelligence (AI) applications in dermatology, approved by the United States (U.S.) Food and Drug Administration (FDA) and international organizations, evaluating their clinical implementation and impact on healthcare delivery. We identified fifteen regulatory-approved AI devices globally, including three FDA-approved systems in the U.S. The FDA-approved devices primarily focused on melanoma and skin cancer detection through specialized hardware, while international platforms emphasized broader applications, mobile accessibility, and condition-specific tools for managing various skin conditions. Beyond these specific tools, we analyzed how AI can enhance clinical dermatology through screening systems, diagnostic support, administrative automation, and practice optimization. AI's integration into medical education can provide immediate feedback, support resident training, and complement traditional instruction, while patient education applications can improve treatment adherence through personalized content delivery. While AI shows promise across these domains, successful implementation requires addressing challenges in representation disparities, data privacy, algorithmic fairness, and clinical workflow integration. Future development should focus on standardized validation protocols, diverse training sets, robust real-world studies, and comprehensive assessment of patient outcomes beyond traditional performance metrics. AI's role appears most effective as augmentation to clinical expertise, particularly in improving access to specialized care and supporting clinical decision-making.