India’s Framework for Responsible AI in Healthcare

Context

At the India AI Impact Summit 2026, the Union Health Minister unveiled two flagship initiatives—SAHI and BODH—to institutionalise the safe, ethical, and accountable application of Artificial Intelligence in India’s healthcare ecosystem.

Overview of the New AI Health Frameworks

Nature of the initiative:
The Government of India has rolled out two complementary national-level frameworks:

SAHI – Strategy for Artificial Intelligence in Healthcare for India
BODH – Benchmarking Open Data Platform for Health AI

Purpose:
These frameworks seek to establish a trust-based, evidence-driven, and transparent AI governance architecture for healthcare delivery and innovation.

Implementing Authority: Ministry of Health and Family Welfare (MoHFW).

SAHI: National Strategy for Ethical Health AI

Core Idea:
SAHI functions as a policy and governance blueprint for integrating Artificial Intelligence into healthcare in a responsible and citizen-centric manner.

Primary Objectives:

Ethical deployment of AI tools: Ensure fairness, transparency, and accountability in AI-driven health applications.
Alignment with national health priorities: Promote innovation while protecting patient data and public interest.

Salient Components:

Regulatory Architecture: Defines policy norms and institutional responsibilities for AI adoption at both Central and State levels.
Responsible AI Principles: Emphasises informed consent, algorithmic explainability, and safeguards against discriminatory outcomes.
System Compatibility: Ensures AI solutions are interoperable with India’s digital health ecosystem, including ABDM-linked platforms.
Collaborative Ecosystem: Promotes partnerships among government agencies, research institutions, startups, and private sector players.
Strategic Scaling Plan: Offers a long-term vision for expanding AI use without compromising safety or public confidence.

BODH: National Validation Platform for Health AI

Concept:
BODH is a centralized testing and benchmarking mechanism to assess the performance and reliability of AI-based healthcare solutions prior to nationwide rollout.

Institutional Support:
Developed by IIT Kanpur in partnership with the National Health Authority.

Key Objectives:

Pre-deployment assessment: Ensure clinical accuracy, safety, and robustness of AI tools.
Bias mitigation: Improve inclusivity by testing AI systems on anonymized real-world datasets.

Major Features:

Open Benchmarking System: Uses diverse and anonymized health data to evaluate AI models across population groups.
Operational Performance Checks: Tests consistency, accuracy, and stability of AI applications in real healthcare settings.
Risk and Bias Detection: Identifies unintended harms and algorithmic distortions at an early stage.
Clinical Suitability Review: Confirms alignment with established medical protocols and public health needs.
Uniform Evaluation Standards: Introduces standardized national benchmarks for quality assurance in health AI solutions.

Source : PIB