India is taking a significant leap toward responsible innovation. On February 17, 2026, at the India AI Impact Summit in New Delhi, the government launched two major national initiatives – SAHI and BODH – aimed at creating a structured, ethical, and technically sound ecosystem for artificial intelligence in healthcare.
The move signals something important. India wants to embrace AI’s potential in medicine, but not at the cost of patient safety or regulatory oversight. Officials described it as walking the tightrope between encouraging innovation and ensuring accountability.
SAHI: The Policy Blueprint for AI in Healthcare
Let’s start with SAHI, which stands for Strategy for Artificial Intelligence in Healthcare for India. Think of it as the rulebook – the governance framework that tells everyone how AI should behave in hospitals and clinics.
Risk-Based Classification
Not all AI tools are created equal. A chatbot that helps patients schedule appointments carries far less risk than an AI system that reads X-rays or recommends cancer treatments. SAHI recognizes this reality.
The framework categorizes AI tools based on their level of clinical impact and associated risk:
- Lower-risk applications – Administrative support tools, appointment schedulers, basic health information chatbots
- Higher-risk applications – Diagnostic systems, treatment decision support, clinical workflow tools that directly impact patient care
This classification matters because it determines how much oversight a particular AI tool requires. Higher the risk, tighter the scrutiny.
Working Within Existing Laws
Here’s something smart about SAHI. Instead of pushing for entirely new legislation, it aligns AI governance with legal frameworks already in place, including the Digital Personal Data Protection Act.
This approach avoids creating regulatory chaos. Hospitals and developers don’t have to navigate completely unfamiliar territory. The rules build upon what already exists, ensuring accountability without duplication.
Core Principles That Guide Everything
SAHI rests on several fundamental principles that every AI tool in healthcare must respect:
- Transparency – Algorithms shouldn’t be black boxes. How they work, what data they use, their limitations – all of this should be clear
- Accountability – When something goes wrong, someone must answer for it
- Patient-centric design – The patient’s wellbeing comes before technological convenience
- Fairness and bias mitigation – AI shouldn’t discriminate against certain groups or communities
The ultimate goal is simple but profound. Build trust among doctors, hospitals, and patients so that AI becomes a trusted partner in healthcare rather than a feared replacement.
BODH: The Technical Backbone for AI Validation
If SAHI is the rulebook, BODH is the testing ground. Short for Benchmarking Open Data Platform for Health AI, this initiative was developed by the Indian Institute of Technology Kanpur in collaboration with the National Health Authority.
Privacy-Preserving Sandbox
Here’s how BODH works. It provides a secure environment where AI developers can bring their models and test them against diverse healthcare datasets. But here’s the crucial part – sensitive patient data isn’t directly exposed during this process.
This privacy-preserving approach means developers can validate their algorithms without compromising the confidentiality of patient information. It’s a win-win. Innovation gets tested. Privacy stays protected.
Integration with Ayushman Bharat Digital Mission
BODH isn’t floating in isolation. It’s integrated under the Ayushman Bharat Digital Mission, which means it connects with India’s broader digital health ecosystem.
The platform offers standardized methods to evaluate:
- Model performance – Does the AI actually do what it claims?
- Bias detection – Does it work equally well across different populations?
- Clinical readiness – Is it ready for real hospitals and real patients?
- Deployment safety – What could go wrong once it’s in use?
This creates something India has lacked until now – a transparent approval pathway for AI tools before they get deployed at scale in hospitals.
Filtering Out Exaggerated Claims
Anyone following the AI space knows the problem. Vendors make grand claims about their products. Performance metrics look impressive in controlled settings. But do these tools hold up in the messy reality of Indian hospitals?
BODH aims to solve exactly this problem. Only rigorously validated tools will move forward into clinical use. The platform acts as a filter, keeping unreliable or poorly performing systems out of patient care pathways.
Support System: Centres of Excellence
SAHI and BODH aren’t working alone. They’re supported by Centres of Excellence at some of India’s premier medical institutions:
- All India Institute of Medical Sciences (AIIMS), Delhi
- Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh
These institutions focus on developing indigenous AI solutions tailored specifically to Indian healthcare needs. Because let’s face it – healthcare challenges in India aren’t always the same as those in Western countries. Solutions need to be homegrown.
All deployments are required to adhere to ethical standards laid down by national medical research guidelines. Patient safety and fairness aren’t optional extras. They’re mandatory.
Key Takeaways: SAHI and BODH at a Glance
- SAHI – Governance and policy framework for AI in healthcare
- BODH – Technical validation platform for testing AI models
- Risk-based classification – Higher-risk AI tools face stricter oversight
- Privacy preservation – BODH tests AI without exposing patient data
- Integration – Connected with Ayushman Bharat Digital Mission
- Validation mandate – Only rigorously tested AI tools enter clinical use
- Centres of Excellence – AIIMS Delhi and PGIMER Chandigarh lead indigenous development
Why This Matters Right Now
India’s healthcare system is digitizing at remarkable speed. AI tools aren’t futuristic speculation anymore. They’re already being tested and deployed for:
- Tuberculosis risk prediction – Identifying high-risk populations for targeted screening
- Diabetic retinopathy screening – Analyzing eye images to detect diabetes-related damage
- Radiology workflow support – Helping radiologists prioritize urgent cases
- Hospital resource optimization – Predicting patient inflows and managing bed availability
All of this is happening now. And without proper guardrails, the risks are real. Biased algorithms could deny care to certain groups. Poorly validated tools could make dangerous recommendations. Opaque systems could hide errors until it’s too late.
SAHI and BODH are designed to prevent exactly these scenarios.
What Experts Are Saying
Health policy analysts see this structured approach as potentially game-changing. India isn’t just adopting AI – it’s building the infrastructure to adopt AI responsibly.
The combination of policy guidance through SAHI and technical validation through BODH creates a comprehensive ecosystem. Developers know what’s expected. Regulators know what to monitor. Hospitals know what to trust. Patients know their safety matters.
Some experts believe this could position India as a global model for ethical AI in healthcare. Other countries watching from the sidelines might find themselves following a similar path.
Looking Ahead
The launch is done. The frameworks are out. Now comes the hard part – implementation.
Centres of Excellence need to scale up their work. More AI developers need to engage with BODH. Hospitals need to understand what compliance looks like. Regulators need to build capacity for oversight.
None of this happens overnight. But the direction is clear. India wants AI in healthcare, but only on its own terms – safe, transparent, accountable, and fair.
For patients, that’s exactly the right approach. For innovators willing to play by the rules, the opportunities remain enormous. And for the healthcare system as a whole, this could mark the beginning of a new era where technology and trust go hand in hand.