Integrating Artificial Intelligence and Ayurvedic Diagnostic Methods in Dermatology: A Narrative Review of Current Evidence and Future Directions

Abstract

Background: Skin disorders continue to present significant healthcare challenge due to their diverse clinical manifestations and need for precise identification. The field of dermatology is seen significantly influenced with recent advancement of Artificial Intelligence (AI) by aiding in image interpretation, pattern recognition, and clinical decision-making. Unlike standardized diagnostic models, Ayurveda focuses on the individual, combining the evaluation of clinical characteristics with patient-specific factors to arrive at a comprehensive diagnosis. Aims and objectives: This review aims to provide an overview of AI-based and Ayurvedic diagnostic methods used in dermatology, emphasizing their strengths and examining their potential to complement each other for more accurate skin disease diagnosis. Materials and Methods: A comprehensive review of literature was conducted using electronic databases such as PubMed and Google Scholar, incorporating relevant peer-reviewed publications. Articles published between 2017 and 2023 were identified using various keywords as artificial intelligence, dermatology, Ayurveda, skin disorders, and diagnostic methods. The selected studies were then reviewed to assess existing evidences and identify the recent advancements and emerging trends. Results: Existing evidences suggest that AI has significantly advanced dermatology by supporting image-based diagnosis, lesion classification, and disease prognosis. In contrast, Ayurvedic diagnostic methods emphasize comprehensive clinical assessment and individualized patient evaluation. Despite the potential of combining these approaches, the integration of AI with Ayurvedic Dermatology remains unexplored. Conclusion: The synergy between AI technologies and Ayurvedic diagnostic principles has the potential to provide more comprehensive approach to dermatological assessment. Further research is needed to develop standardized frameworks and evaluate the feasibility and effectiveness of applying these integrated methods in clinical dermatology practice.