There is a huge opportunity to use artificial intelligence in public health, but it will require building a digital database of patients first, said Dr Soumya Swaminathan, chairperson of the MS Swaminathan Research Foundation, at an event in Chennai on Saturday. Her comments come as private hospitals already use AI to track patient data and outcomes, while public systems lag behind.
Speaking at an event organised by Super Chennai, a social impact initiative of CREDAI Chennai, Dr Swaminathan-former chief scientist at the World Health Organization-stressed that the technology must be deployed responsibly. "AI, before deployment in health, needs to be tested the same way as drugs or vaccines," she said.
Building the digital backbone
Dr Swaminathan noted that private hospitals are using AI effectively to track data and gain insights into health outcomes. "But in the public health system, there is a need to create a digital database of patients. Then, we can start tracking, monitoring and support extension," she said. This push for a digital patient database connects to broader work in AI for Healthcare and AI for Government, where data infrastructure is a critical first step.
The former WHO chief scientist also cautioned against using AI merely for the sake of it. Rigorous testing, similar to pharmaceutical trials, should precede any deployment in clinical settings.
Field experience shapes policy
Beyond technology, Dr Swaminathan urged professionals to work alongside government to address environmental crises. "Getting on the field will help understand the subject and ground reality better," she said. She added that insights from people directly affected-whether fishers, tribal communities, or the elderly-help build evidence-backed policies.
Super Chennai managing director Ranjeeth Rathod said the award recognised Dr Swaminathan's legacy of science, compassion and public service, aiming to inspire future generations.
Why this matters for healthcare professionals
For healthcare leaders and practitioners, Dr Swaminathan's call highlights a dual challenge: building the data infrastructure that makes AI possible in public health, and insisting on the same evidentiary standards for algorithms as for drugs. Without digital patient records, even the most advanced AI tools cannot deliver on their promise in under-resourced settings. The message is clear-invest in the foundations before chasing the technology.
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