The launch of the SAHI and BODH initiatives

The launch of the SAHI and BODH initiatives at the India AI Impact Summit 2026 marks a pivotal shift toward a regulated, ethical, and high-tech healthcare ecosystem in India.

Under the aegis of the Ministry of Health and Family Welfare (MoHFW), these frameworks are designed to bridge the gap between rapid AI innovation and patient safety.

SAHI: Strategy for AI Healthcare in India:

SAHI serves as the policy “compass” for the nation. It is a governance framework designed to ensure that AI integration is not just fast, but responsible and people-centric.

Core Objectives:

• Ethical Deployment: Creating a transparent environment where AI is accountable for its “decisions.”

• Alignment: Ensuring private-sector innovation matches India’s specific public health priorities.

• Data Sovereignty: Protecting patient privacy through strict consent-based protocols.

Key Features:

• Unified Governance: Sets national standards to ensure AI tools work consistently across different Indian states.

• Interoperability: Designed to plug directly into the Ayushman Bharat Digital Mission (ABDM), allowing for seamless data flow.

• Bias Prevention: Establishes mandates to audit algorithms for socio-economic or demographic biases.

• Strategic Roadmap: Provides a long-term plan for startups and tech giants to scale their health-tech solutions safely.

   BODH: Benchmarking Open Data Platform for Health AI

Developed by IIT Kanpur in collaboration with the National Health Authority (NHA), BODH is the technical “filter” or validation engine for AI tools.

Core Objectives:

• Evidence-Based Validation: Moving away from “claims” to “proven results” through rigorous testing.

• Reliability: Ensuring that an AI tool working in a lab also works in a crowded rural clinic.

Key Features:

• Open Data Benchmarking: Uses massive, anonymized, real-world Indian datasets to test how well an AI performs across diverse populations.

• Risk Assessment: Identifies “black box” risks—where an AI’s reasoning is unclear—before the tool ever touches a patient.

• Clinical Relevance: Checks if the AI actually solves medical problems according to established clinical standards.

• National Norms: Creates a “Gold Standard” for health AI, making it easier for high-quality Indian startups to get certified.

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