By Dr. Radhakrishna B., Director – Customer Advisory, SAS
Across the world, public health systems are undergoing a profound transformation. The shift is not merely technological but structural as well. We are moving from a world where health departments collect data in isolation to one where they must make sense of it, act on it, and deliver outcomes at scale.
Yet despite unprecedented data abundance, most health agencies still struggle with fragmentation. Information sits in departmental silos, limiting the ability to build a complete picture of a citizen’s health journey or the systemic vulnerabilities that must be addressed.
This reality is echoed globally, and India is no exception. But what gives India a distinct advantage is the strength of its digital public infrastructure. Over the past decade, the country has built robust platforms like Aadhaar, CoWIN, Ayushman Bharat Digital Mission and more that reach citizens at population scale. As we look ahead to the next decade of public health innovation, this foundation allows us to leapfrog: to move quickly from digitization to intelligent automation and proactive, real-time decision support.
What is changing most significantly is the mindset around adoption. Health departments are no longer debating whether to embrace AI, they are recognizing the need to automate as much as possible.
Processes that were once manual, inconsistent, and resource-intensive now demand intelligent systems that can sift through data, detect anomalies, and provide guidance instantly. And yet, despite this progress, many health departments still lack the infrastructure frameworks necessary to sustain the AI models they aspire to build. A model is not a standalone artefact; it is a living system that requires continuous data pipelines, monitoring, governance, and adaptation. Without these elements, even the most promising algorithms remain pilots that never scale.
This is where India’s digital strength becomes crucial. With reliable identity systems, interoperable health records, and strong connectivity, India is uniquely positioned to embed AI into healthcare delivery in a way that is equitable and far-reaching. The next phase of progress will involve not just centralized intelligence, but intelligence at the edge. Edge computing has the potential to transform public health by enabling data to be gathered, managed, and analyzed where it is generated. In a country as large and diverse as ours, that proximity can be the difference between a delayed response and a life saved. It reduces friction, eliminates latency, and ensures that insights travel faster than the challenges they aim to solve.
This vision is already evident in state-level health initiatives where organizations like SAS are helping apply AI to reinforce healthcare schemes for citizens. These programs process thousands of claims every day, and with that volume comes the inherent risk of fraudulent or fictitious submissions. AI systems are now being used to evaluate claim documents at scale, detecting anomalies that may indicate abuse or wastage and ensure that benefits reach the intended individuals. The impact goes beyond savings. It restores trust, improves efficiency, and reinforces the integrity of public health welfare.
Another example is how we leveraged the use of causal analytics to address infant and maternal mortality. By analyzing patterns across maternal health data, clinical records, and environmental factors, AI models can help identify high-risk pregnancies much earlier. These insights enable targeted interventions, ensuring that the most vulnerable women receive timely care. When technology can predict risk before it becomes a crisis, it expands the reach of frontline workers and saves lives that might otherwise be lost to delayed diagnosis or inadequate followup.
But as we embrace these capabilities, one principle must guide every step: trust. Public health operates at the intersection of science, policy, and human vulnerability. People share their most sensitive information with the expectation that it will be used ethically. That is why guardrails, fairness, and governance must be woven into the lifecycle of every model, from development to deployment. Responsible AI is not an optional layer; it is the foundation on which sustainable public health transformation must be built.
India can define what the future of public health AI looks like: inclusive, transparent, equitable, and deeply human-centered. If we get the infrastructure, governance, and intent right, AI will not just optimize our systems, it will strengthen the very fabric of public health and touch the lives of millions.