Conscious digital health: Integrating Ayurvedic epistemology with Artificial Intelligence

Background: Digital health can flatten traditional medical knowledge into reductionist codes. Ayurveda locates valid knowledge (Pramāṇa) at the intersection of experience, environment, and awareness – dimensions not exhausted by quantitative computation. Aim and Objective: The aim of the study was to present Structured Distributed Introspection (SDI) as an epistemic framework that operationalizes a perception–reflection–reintegration loop for semantic coherence and to embed it within Collaborative Medicine and Science (CoMS), a translational methodology (Reformulation → Modeling → Localization) that preserves Ayurvedic meaning while enabling computation. Materials and Methods: Conceptual synthesis integrating classical Ayurvedic epistemology with embodied/active-inference theories, anchored to current Ayush/World Health Organization (WHO) standards (ICD-11 TM2, NAMASTE, Ayush Grid). An SDI validation summary is provided from dual-protocol studies across 10 Large Language Models (LLMs; n = 400 responses), quantifying cross-frame consistency. Results: SDI formalizes introspection as structural feedback; within CoMS, it supports (a) semantically faithful, computable Prakṛti/Vikṛti assessment; (b) prodrome-aware saṃprāpti modeling; (c) adaptive learning mirroring introspective practice; and (d) methodical guardrails for Artificial Intelligence (AI) use in Ayurveda. Limitations: SDI’s engineering/architectural phase is a prospective research program; empirical validation beyond LLM self-description requires multicenter studies and clinician-rated endpoints. Conclusion: SDI (epistemic core) within CoMS (translational core) provides a rigorous, concept-first pathway for digitizing Ayurveda that enables interoperability without erasing identity, aligning practice with WHO principles for responsible, culturally grounded AI.