Artificial Intelligence in Dermatology: Current Applications and Future Directions — A Systematic Review

Abstract

Artificial intelligence (AI) has rapidly emerged as a transformative technology in dermatology, a specialty inherently suited for image-based analysis. The increasing availability of large dermatological image datasets, advances in deep learning algorithms, and improvements in computational power have enabled the development of automated systems capable of diagnosing skin diseases with high accuracy. This systematic review aims to evaluate the current applications, diagnostic performance, limitations, and future potential of AI in dermatology. A systematic literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar databases following PRISMA guidelines. Studies evaluating AI algorithms in dermatologic diagnosis, disease severity assessment, dermatopathology, teledermatology, and treatment prediction were included. Evidence suggests that deep learning models, particularly convolutional neural networks (CNNs), demonstrate diagnostic accuracy comparable to dermatologists in certain tasks, such as melanoma detection and lesion classification. AI applications also extend to dermatopathology image analysis, psoriasis severity scoring, acne grading, and teledermatology triage systems. Despite its significant potential, the clinical implementation of artificial intelligence (AI) in dermatology faces several important challenges, including algorithmic bias due to underrepresentation of darker skin phototypes, limited external validation, ethical concerns regarding patient privacy, and regulatory barriers. In this systematic review, 78 eligible studies were analyzed to evaluate the current evidence regarding AI applications in dermatology. The reviewed studies collectively demonstrate that AI-based systems, particularly deep learning models, show promising diagnostic performance in skin cancer detection, dermatopathology, disease severity assessment, and teledermatology. However, considerable heterogeneity exists among studies with respect to datasets, methodologies, validation techniques, and clinical applicability. While AI technologies have the potential to improve diagnostic efficiency and expand access to dermatologic care, current evidence suggests that these systems should be considered supportive clinical tools rather than replacements for dermatologists. Further large-scale, multicenter studies with diverse patient populations and standardized validation protocols are required before widespread clinical integration can be achieved