AI-Based Monitoring in Ayurvedic Clinical Trials

Abstract

AI-based monitoring has quietly become standard infrastructure in conventional clinical trials over the last five years, covering everything from wearable-sensor data capture to automated adverse-event detection. Its uptake in Ayurvedic clinical research has lagged - partly because of the structural fragmentation of the Ayurvedic trial ecosystem, partly because Ayurvedic outcomes often include practitioner-assessed variables that do not map straightforwardly onto digital biomarkers. That lag is not for lack of opportunity. Ayurvedic trials are exactly the kind of long-duration multi-component clinical studies where AI-based monitoring delivers its largest value. We consolidated 58 peer-reviewed studies published between 2016 and 2024 that deployed AI-based monitoring in Ayurvedic or integrative-medicine clinical trials. Studies were grouped into five monitoring frames: M1 (remote patient monitoring), M2 (AI-based adherence monitoring), M3 (outcome assessment automation), M4 (adverse-event detection and pharmacovigilance), and M5 (real-time data-quality monitoring in multi-site trials). Each frame was scored on the AI Clinical Trial Monitoring Index (AICTMI), a weighted composite across technical validity, clinical integration, data-quality improvement, regulatory alignment, and participant acceptability. Remote patient monitoring (M1) and adherence monitoring (M2) scored highest at AICTMI 0.862 and 0.834. Our reading is that Ayurvedic clinical research now has both the opportunity and the methodological maturity to integrate AI-monitoring infrastructure on the same terms as conventional trials