Introduction: India’s Dual Mission
India is racing to become a global AI powerhouse while simultaneously attempting to fix one of its most fragmented systems: healthcare. The intersection of these two ambitious goals is where India’s boldest bets—and biggest risks—are playing out in real-time.
Abhishek Singh, Director General of the National Informatics Centre (NIC), Additional Secretary at MeitY, and CEO of the IndiaAI Mission, isn’t trying to sell a vision. He’s focused on making sure it works. In a rare unscripted conversation at the grand finale of CNBC-TV18 HealthX Elevate, presented by Optum India, he laid out what the government is enabling, where it’s stepping back, and why trust—not just technology—will ultimately define India’s healthcare AI story.
The conversation, moderated by CNBC-TV18’s Ashmit Kumar, touched on infrastructure, regulation, digital health innovation, and the growing influence of global capability centers (GCCs). Singh didn’t shy away from the contradictions. India has the talent, the data, and the ambition. But it also has gaps in safeguards, public trust, and real-world deployment.
AI Outperforming Human Diagnostics
The session opened with a compelling anecdote: a senior official suffers a health scare, a CT scan is conducted, and a Bengaluru startup analyzes the scan with AI, finding something the initial read missed. It was a useful starting point, posing the question many in the room were already thinking: Is this the future?
Singh didn’t hedge. “In some cases, like tuberculosis and in some ailments, the AI-generated outcomes might be better than some of the radiologists,” he said. Not all—but some. The difference, he noted, is in the dataset. While one radiologist brings their individual experience, an AI model trained on thousands of cases across institutions brings something else: cumulative expertise.
Regulatory Approval for AI Models
That’s already starting to show up in practice. India’s health regulators—including ICMR and CDSCO—have approved AI models for use in diagnostic protocols. The logic is straightforward: if AI can assist, augment, or even outperform in limited contexts, it should be used—provided it’s tested, certified, and implemented with care.
Balancing Public Sector Needs with Private Innovation
When Singh talks about AI in healthcare, he speaks like someone who has watched systems break down under volume. Whether it’s AI-driven solutions helping public hospitals better manage OPD overload, or ICUs in remote areas that function entirely on telemedicine protocols, he sees AI less as an upgrade and more as a way to keep the system from collapsing under its own weight.
India’s Unique Healthcare Duality
That’s what makes India an unusual test case. On one hand, you have high-end startups solving for speech-to-text clinical notes, multilingual doctor-patient translation, and autonomous radiology screening. On the other hand, you still have patients dying from untreated conditions because there’s no doctor or information within reach.
That duality is where Singh sees value—tools like NLP-based voice bots that handle health queries in regional languages, or AI triage systems that help non-clinicians recommend the right tests.
The IndiaAI Mission Investment Strategy
But scaling those systems won’t be driven by government budgets alone. Instead, Singh sees the government playing enabler. “We did a lot of consultations across the industry,” he said. “And the feedback was clear. We need to invest more in compute, in R&D, in building datasets, and in supporting foundation models. Those are the objectives of the IndiaAI Mission.”
The numbers are impressive. The government has committed ₹10,000 crore to the initiative. But private players have already invested the equivalent of ₹20,000 crore to bring in 38,000 GPUs. Singh was clear about the intent: by subsidizing access, the government enables private investment that outstrips its own spending.
“Private players put in ₹20,000 crore to bring those GPUs. We’re subsidizing access—so startups don’t burn all their capital just trying to train a model.” It’s a strategy designed to lower the barrier to entry without centralizing control.
Building Trust Through Technology
One of the sharpest analogies Singh offered came when the conversation turned to public adoption. When asked if Indians are ready to trust digital health tools, Singh talked about UPI. “When a vegetable vendor hears the ₹20 payment has gone through, sees the tick mark, he doesn’t even check. That’s how much trust there is in UPI. And it’s been built at the very bottom of the pyramid,” he said. “If we can do that in payments, we can do it in healthcare too. But we have to earn that trust.”
The Role of Regulation in Building Confidence
And that trust, Singh made clear, can’t be separated from regulation. He doesn’t argue against AI, but rather, insists on responsible rollout. Just like a new drug or vaccine, Singh argued, AI models in healthcare should only be deployed after proper testing and regulatory approval.
That framework, Singh suggested, doesn’t exist in full today. While the RBI has already released a draft Responsible AI framework for financial services, healthcare still doesn’t have an equivalent. That, he said, needs to change urgently.
Data Privacy and Regulatory Framework
No conversation about healthcare data is complete without addressing privacy. Singh acknowledged that the Digital Personal Data Protection (DPDP) Act is a crucial step in the right direction. “We’re waiting for the rules just like everyone else,” he said. “The indication is—it could come any day.”
How DPDP Will Govern Healthcare AI
In the meantime, Singh outlined how the Act would apply to AI models:
- No sharing of personal health data without consent
- No processing beyond the purpose it was given for
- No retention beyond the duration approved
In other words, the same rules that govern human actors will apply to algorithms—once the framework is active.
India’s Global Healthcare AI Leadership
When we think in the context of designing AI systems, India’s healthcare system doesn’t just have limitations—it has relevant constraints. A multilingual population. Shortage of doctors. Patchy infrastructure. Many without access to doctors, diagnoses, or basic information. And a cost structure that rules out most Western tech.
Design Specs for the Global South
These are the design specs for the future of healthcare innovation—not just in India, but across the Global South. If India can build systems that work here—multilingual, infrastructure-light, clinician-agnostic, and affordable at scale—they won’t just be India-ready. They’ll be world-ready.
That’s why Singh emphasized not just engineering capability, but ownership as well. “Our engineers have contributed to the growth and expansion of almost every big tech company across the world,” he noted, “which is why it is our priority to provide any support that they may need to build products in India and not to leave India and go and contribute elsewhere.”
He expressed confidence in India’s ability to create the next wave of AI products, “Made in India, and soon to become global commodities in the days to come.”
Conclusion: Reshaping Healthcare Worldwide
That’s the bar, and that’s the opportunity. India’s approach to healthcare AI isn’t just about solving domestic challenges—it’s about creating scalable, affordable, and accessible solutions that can transform healthcare delivery across developing nations.
When India succeeds in building trust-based, regulation-backed, and culturally appropriate AI healthcare systems, it won’t just change healthcare domestically. It will reshape what healthcare means everywhere, particularly in resource-constrained environments that serve billions of people globally.
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