Use of artificial intelligence in Ayurvedic dermatology: Diagnosis and management of skin disorders

Background: Artificial Intelligence (AI) is increasingly transforming dermatology by improving diagnostic accuracy and efficiency. Concurrently, Ayurveda-the ancient Indian medical system-offers a personalized, holistic approach to skin disorders (Twak Vikara). However, integration of AI with Ayurvedic dermatology remains nascent. Objectives: We systematically reviewed recent literature (2020-2025) on applications of AI in skin disease diagnosis and management, with a focus on intersections between modern dermatology and Ayurvedic principles. Methods: A systematic search was conducted in PubMed, Scopus, Google Scholar, the AYUSH research portal, and DHARA using keywords combining “Artificial Intelligence” or “Machine Learning” with “Ayurveda” and “Dermatology” or specific skin conditions (e.g., “Twak Vikara,” “psoriasis,” “acne”). Inclusion was limited to peer-reviewed articles from January 2020 to July 2025. Relevant studies on AI in dermatology, Ayurvedic approaches to skin diseases, and especially those bridging both fields were analyzed per PRISMA guidelines. Results: Out of 62 records identified, 18 articles met inclusion criteria (5 on AI in modern dermatology, 7 on Ayurvedic dermatology, 6 directly integrating AI with Ayurveda). AI has demonstrated dermatologist-level performance in detecting skin lesions (e.g., melanoma) and improved teledermatology outcomes in primary care. Ayurveda literature describes skin diseases like eczema (Vicharchika), psoriasis (Kitibha), and acne (Yuvan Pidika) in terms of doshic imbalance, with individualized treatments showing efficacy in case reports. A few pioneering studies combined these domains: one 2023 study using deep learning to classify skin diseases and then provide Ayurvedic treatment suggestions achieved high accuracy (>95%) in disease identification and recommendation success. Nonetheless, no large clinical trials were found. Key themes include AI’s strength in image-based diagnosis, Ayurveda’s holistic treatment protocols, and early attempts at AI-driven Ayurvedic diagnostic tools. Conclusion: AI holds promise to enhance Ayurvedic dermatology by objectively diagnosing skin conditions and personalizing therapy recommendations, while Ayurveda’s rich clinical knowledge can broaden AI’s scope in holistic skincare. Robust interdisciplinary research and clinical validation are needed to realize an integrative model that leverages AI’s precision and Ayurveda’s personalization for improved skin health outcomes.