India unveils SAHI, a strategic framework for responsible AI in healthcare, centering patient care, equity, transparency, and public trust as it guides implementation.

The broader vision calls for diverse health data, ethical stewardship, and a uniquely Indian AI-for-health ecosystem shaped by ongoing government, industry, and international collaboration to move AI from promise to practice.

SAHI rests on five core pillars—from governance and evidence generation to safe digital/data infrastructure and workforce readiness—designed to enable risk-proportionate governance and strong data foundations.

Experts highlight the importance of data diversity in genomics, high data quality, strategic private partnerships, and involving health professionals to generate real-world evidence and inform policy.

A strong emphasis on data diversity and governance to mitigate bias, protect privacy, and ensure accountability in AI models.

The program envisions rapid insight extraction from research and aims to aggregate genomic data from tens of millions to strengthen AI in health, while building a workforce adept at collaborating with AI to deliver universal, affordable care.

Governance gaps identified include the need for backbone capacity—national regulatory and institutional readiness—and a Duty of Care to protect public trust and ensure equity in AI health applications.

Experts urge closing governance gaps through a Duty of Care and a committed backbone for regulatory and institutional readiness, with developers, deployers, and government safeguarding trust and patient safety.

Policy components include the Ayushman Bharat Digital Mission (ABDM) with hundreds of millions of digital health records and interoperable data exchange that enable AI tools for surveillance and TB screening.

ABDM's infrastructure features longitudinal records, registries, consent-based data exchange, and supports AI applications such as disease surveillance and TB detection via AI-assisted chest X-ray interpretation.

Outcomes of ABDM and data interoperability lay the groundwork for AI integration and real-world health applications, with ongoing AI-enabled tools for surveillance and TB screening.

Global collaboration and ethics are central, with WHO and ICMR emphasizing ethical stewardship, the Duty of Care, and alignment with global norms to build trust and encourage responsible investment in health AI.