4 minutes of readingUpdated: February 18, 2026 at 04:33 IST
Deploying AI solutions in healthcare will require a “lifecycle” approach, starting from problem definition to data collection, storage and management, verification and validation, and real-world performance, according to a policy framework for the sector released by the Union Health Ministry on Tuesday at the AI Impact Summit.
“Once you have developed an AI solution, how do you procure it, how do you monetize it? There is no way to determine that in the current procurement mechanisms and methods,” said Sunil Kumar Barnwal, CEO of the National Health Authority.
Elaborating on the need for such a framework, he said: “One of the biggest challenges of AI is that it learns as it is used, but it can also sometimes drift. These challenges can only be addressed when we look at the entire life cycle of AI, from data collection to training to deployment, continuous monitoring and decommissioning if necessary.”
Barnwal led the committee that developed the framework called Strategy for Artificial Intelligence in Healthcare in India (SAHI). This is the broader framework for using AI in healthcare, with an implementation roadmap that outlines which department is doing what is currently underway, he said.
The framework states: “AI systems in healthcare differ fundamentally from conventional digital tools: they influence clinical judgment, shape care pathways, and inform decisions at the population level, often evolving through updates or learning mechanisms. Therefore, the adoption of AI in healthcare cannot be treated as a one-time approval or deployment event.”
According to Barnwal, any AI solution in healthcare will have to follow the principles that are already part of the Ayushman Bharat Digital Mission (ABDM). Data privacy will need to be built into any solution by design, he said, adding that patient consent would be required for data sharing.
“Data under ABDM resides at the source, whether it is a hospital, laboratory or pharmacy. It does not migrate to a central database. And the patient must give consent every time the data moves from one location to another,” he said.
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Training health workers is also critical to the effective deployment of any AI solution, Barnwal said.
Union Health Minister JP Nadda said India laid the foundation for AI years ago. “We all know that AI does not work in isolation. AI lives, breathes and thrives on digital infrastructure. Realizing this early on, India started laying the foundation almost a decade ago. In 2015, we launched Digital India with a clear objective: to make India a digitally enabled society and a knowledge economy,” he said.
He said ABDM created the digital public architecture, without which AI could not be developed responsibly or at scale. Nadda has also launched a system that will allow innovators to train their healthcare AI models on real data made available on a single platform.
Dr Catharina Boehme, WHO-SEARO, said: “This is not just a technology roadmap, it is a public health strategy designed to strengthen care, improve decisions, expand reach. It supports progress towards universal health and the Sustainable Development Goals… India has become the first country in the South-East Asia region to adopt such a comprehensive strategy and one of the first countries in the world to have it. India has established a benchmark.
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The framework was developed through four major rounds of deliberations held in Vijayawada, DelhiShillong and Mumbai with clinicians, health technology companies, state governments, healthcare delivery participants, and policy makers.
Manindra Agarwal, director, IIT Kanpur, said, “The main challenge for the team was that the actual data on which the AI has to be trained is very fragmented, available across various health centers and in small quantities. The data also needs to be well protected due to privacy concerns. So sharing it is not easy.”
Agarwal also spoke about the challenges faced while developing a platform called Benchmarking Open Data Platform for Health AI (BODH) by the government and IIT.
Kanpur. “Once the data is on the platform and every developer wants to train their model, the data no longer adds value after a while because it’s the same data. So you need a mechanism to ensure that the data is constantly updated,” he said.
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