Artificial Intelligence in Dermatology: Opportunities, Limitations, Ethical Challenges and Health Equity – A Narrative Review

Background: Artificial intelligence (AI) has become a promising tool in dermatology that could improve diagnostic accuracy, optimize clinical workflows, and expand access to dermatological care. Recent advances in machine learning and deep learning have enabled the development of highly accurate image-analysis systems, particularly for the diagnosis of skin lesions. However, the implementation of AI in clinical practice is accompanied by important ethical, legal, and equity-related challenges. Objective: This narrative review aims to summarize the current evidence regarding the applications of artificial intelligence in dermatology, with particular emphasis on its potential to improve healthcare accessibility, support teledermatology, and promote health equity while addressing existing limitations and ethical concerns. Methods: A narrative review of the literature was conducted using PubMed, PubMed Central, and Google Scholar. Peer-reviewed articles published in English between 2017 and 2025 were included. The literature was selected using keywords related to artificial intelligence, dermatology, skin cancer, teledermatology, and health equity. Results: The reviewed literature demonstrates that AI has achieved high diagnostic performance in the recognition of various skin diseases, particularly malignant skin lesions, and may significantly improve access to dermatological services through teledermatology and decision-support systems. Nevertheless, challenges including algorithmic bias, limited representation of diverse skin phototypes in training datasets, data privacy concerns, and insufficient regulatory frameworks remain substantial barriers to widespread implementation. Conclusions: Artificial intelligence has the potential to transform dermatological care by enhancing diagnostic efficiency and improving healthcare accessibility. However, its successful integration into clinical practice requires diverse and representative datasets, robust ethical and legal standards, and continued human oversight to ensure safe, equitable, and patient-centered healthcare.