India's participatory approach to developing a national AI strategy for health: lessons for collective learning
Year: 2026
Type: article
Abstract: National AI health strategies are proliferating, yet development methodologies remain largely undocumented, limiting collective learning. India's Strategy for AI in Healthcare (SAHI) was developed through a three-phase participatory process spanning situational analysis, regional consultations across four geographic zones, and structured synthesis engaging over 300 stakeholders. Documenting that process architecture for examination and adaptation by other countries is the methodological contribution of this paper. Consultation surfaced five system-level constraints recurring across emerging AI health ecosystems and drove a fundamental restructuring from prescriptive implementation planning to an enabling framework. A dual framework emerged separating technical prerequisites from governance and implementation conditions, with equity embedded as an operational requirement throughout. Rights-based regulation, market-led innovation, and state-centric control have dominated global AI governance debate. SAHI reflects a fourth approach, anchoring governance in shared digital public infrastructure rather than rules alone, and offers a distinct reference point for any country developing a health-specific AI strategy.
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,250
OpenAlex ID: W7203580622