Integration of digital health in Ayurveda and Yoga: Emerging trends, insights and challenges

Ayurveda and Yoga are established traditional medicines that emphasize holistic, person-centered care. In this context, digital health technologies, including Artificial Intelligence (AI), Machine Learning (ML), biosensors, and telemedicine, are increasingly being explored to support evidence generation, service delivery, and health system integration. This narrative review synthesizes current evidence on digital health applications in Ayurveda and Yoga, and examines key challenges related to validation, ethics, regulation, and implementation. A literature search was conducted in PubMed and ScienceDirect for studies published between January 2000 and October 2026 using predefined keywords related to digital health, AI, Ayurveda, and Yoga. Eligible studies included original research, clinical studies, pilot investigations, and relevant reviews. Thirty-one studies met the inclusion criteria and were narratively analyzed. The reviewed literature demonstrated early-stage applications of digital diagnostics, AI-supported clinical decision tools, network pharmacology, and Tele-Yoga interventions. ML algorithms and sensor-based technologies have been explored for the development of Prakriti (~somatic constitution) assessment and Nadi pariksha (~examination of pulse) tools. Computer vision-assisted systems were used to provide real-time, accurate feedback on the correct performance of Asana (~steady, comfortable posture). These approaches showed potential to enhance personalization, scalability, and continuity of care. However, the evidence base remains heterogeneous, with most studies limited to proof-of-concept designs, small datasets, and insufficient clinical validation. Digital health technologies may support the responsible integration of Ayurveda and Yoga into health systems if embedded within robust governance frameworks. Consistent with World Health Organization digital health guidance, future efforts should prioritize standardized validation, equity-oriented data governance, interoperability, and implementation-focused research to ensure safe, effective, and sustainable adoption.