AI in Healthcare

AI in Healthcare will help in strengthening India’s Digital Health Ecosystem. With the launch of SAHI and BODH initiatives, India’s health ecosystem is stepping forward. Read here to learn more.

At the India AI Impact Summit 2026, the Union Health Minister launched two key digital initiatives– Secure AI for Health Initiative (SAHI) and Benchmarking Open Data Platform for Health AI (BODH), aimed at strengthening India’s health AI ecosystem.

These initiatives mark a significant step toward institutionalising responsible, evidence-based and citizen-centric use of Artificial Intelligence in healthcare, aligning with India’s broader digital public infrastructure model.

Why AI in Healthcare?

India faces structural healthcare challenges:

• Low doctor-to-population ratio
• Urban-rural healthcare divide
• Rising burden of non-communicable diseases
• High out-of-pocket expenditure

AI has the potential to:

• Improve early diagnostics
• Support clinical decision-making
• Reduce costs
• Increase accessibility in remote areas

However, unregulated deployment risks privacy breaches, bias, and unsafe medical decisions. SAHI and BODH aim to address this gap.

Secure AI for Health Initiative (SAHI)

SAHI is a governance framework and national roadmap for responsible AI adoption in healthcare.

Key Features:

• Accountability mechanisms
• Privacy protection safeguards
• Ethical AI guidelines
• Citizen-centric oversight
• Alignment with data protection norms

Objective:

Ensure AI deployment is:

• Safe
• Transparent
• Equitable
• Legally compliant

SAHI emphasizes trust-building, which is crucial in healthcare where decisions directly affect life and well-being.

Benchmarking Open Data Platform for Health AI (BODH)

BODH is a validation and benchmarking platform.

Function:

• Test AI models for performance accuracy
• Validate safety and reliability
• Assess real-world readiness
• Provide standardized evaluation protocols

This ensures AI tools are not deployed without rigorous testing.

It acts as a “quality control gatekeeper” before clinical adoption.

Digital Health Backbone

These initiatives build upon:

National Health Policy (2017)

• Emphasizes digital health expansion
• Focuses on universal health coverage

Ayushman Bharat Digital Mission

• Creation of digital health IDs
• Interoperable health records
• National health data architecture

AI applications will operate within this interoperable digital health ecosystem.

Data Governance and Interoperability

A major strength of the SAHI-BODH framework is its focus on:

• Consent-based data sharing
• Secure digital infrastructure
• Standardized data protocols
• Interoperable health records

This aligns with:

• Data minimization principles
• Privacy-by-design architecture
• Ethical AI development

Trust in digital health depends on strong data governance.

Areas Where AI Can Transform Healthcare

Diagnostics

• AI-assisted radiology (X-ray, CT, MRI interpretation)
• Early cancer detection
• Tuberculosis screening in rural areas

Drug Discovery

• Faster molecule screening
• Predictive modeling for clinical trials
• Cost reduction in R&D

Clinical Decision Support

• Risk prediction models
• Treatment pathway optimization
• ICU monitoring systems

Public Health Surveillance

• Disease outbreak prediction
• Epidemic modeling
• Resource allocation planning

Telemedicine & Accessibility

• AI chatbots for primary consultation
• Remote triage systems
• Language translation for regional outreach

Benefits for India

• Affordable Healthcare: AI reduces diagnostic costs and minimizes unnecessary investigations.
• Bridging Rural-Urban Gap: AI tools can empower frontline health workers.
• Strengthening Preventive Healthcare: Predictive analytics enables early intervention.
• Supporting Universal Health Coverage: Improves the efficiency of public health delivery.

Challenges in AI Healthcare Adoption

• Data bias affecting marginalized groups
• Algorithmic opacity (“black-box” models)
• Cybersecurity risks
• Lack of regulatory clarity
• Skill gaps among medical professionals
• Ethical concerns around automation

SAHI attempts to institutionalize safeguards to address these issues.

Significance for Governance

The launch of SAHI and BODH reflects a shift from unregulated innovation to regulated, responsible innovation

It mirrors India’s broader approach seen in:

• Digital Public Infrastructure (DPI)
• Aadhaar-based service delivery
• UPI financial systems

India aims to create a trusted AI ecosystem, not merely a large AI market.

Way Forward

• Capacity building for healthcare professionals
• Clear medico-legal liability framework
• Continuous algorithm audits
• Public-private research collaboration
• International cooperation in AI ethics

Conclusion

The launch of SAHI and BODH marks a decisive step toward embedding AI within India’s healthcare system in a secure, ethical, and evidence-based manner.

By combining governance oversight with technical validation, India seeks to harness AI’s transformative potential while safeguarding patient rights and public trust.

If implemented effectively, AI in healthcare can:

• Improve diagnostic accuracy
• Accelerate innovation
• Reduce costs
• Expand access
• Strengthen public health resilience

AI in healthcare is not just about technology; it is about ensuring that innovation remains people-centric, accountable, and aligned with constitutional values of equity and justice.