India is rapidly transforming its disease-surveillance approach by integrating artificial intelligence, real-time data analytics, and digital-intelligence platforms. The goal is shifting from traditional “detective-style” tracking towards a predictive model — one capable of spotting early warning signs of outbreaks, even before many cases are reported.
At the heart of this transformation is an AI-driven system called Health Sentinel, operated by National Centre for Disease Control (NCDC) under the framework of Integrated Disease Surveillance Programme (IDSP). Since its rollout in 2022, Health Sentinel has processed over 300 million news articles across 13 Indian languages, automatically extracting structured data on health events — disease type, location, scale — from media reports. This automated scanning has dramatically increased detection capacity and reduced the manual workload for surveillance teams — cutting down the effort by about 98%.
The results so far are significant: between 2022 and 2025, Health Sentinel is credited with generating over 5,000 real-time alerts for potential infectious disease outbreaks. Importantly, the system follows a “human-in-the-loop” model: after AI detection, expert epidemiologists review and verify the flagged events before official alerts are issued to health authorities — maintaining a check on false positives and ensuring credibility.
Looking ahead, India’s public-health authorities plan to expand this setup into a wider predictive-surveillance network. The upcoming model aims to combine AI-powered media surveillance with lab intelligence, climate data, population-movement patterns, and digital diagnostics — enabling forecasting of outbreak trajectories, earlier interventions, and stronger district-level risk mitigation.
