India health monitoring

An artificial intelligence–powered surveillance system deployed by the National Centre for Disease Control (NCDC) has significantly strengthened India’s ability to track infectious disease outbreaks, generating more than 5,000 real-time alerts for health authorities since 2022, according to a new pre-print study. Developed by WadhwaniAI, the Health Sentinel platform has automated the labor-intensive task of scanning news reports for unusual health events. The system reportedly reduced manual workload by 98%, enabling faster outbreak detection and quicker public health action. The findings are currently under review and not yet peer-reviewed. Under India’s Integrated Disease Surveillance Programme (IDSP), media scanning and verification has long relied on manual review of print, television and online news. Health Sentinel upgrades this process by screening articles daily across 13 languages, applying AI models to highlight potential threats that are later reviewed by epidemiologists. According to the study, the platform has processed over 300 million news articles since April 2022, identifying 95,000+ unique health-related events, of which around 3,500 were shortlisted by NCDC experts as possible outbreaks. Researchers also estimate that the AI-enabled system triggered more than 5,000 actionable alerts between April 2022 and April 2025. Parag Govil, National Program Lead for Global Health Security at WadhwaniAI, said the tool preserves human oversight while eliminating the time-consuming manual scanning traditionally required. Epidemiologists validate flagged events before disseminating them to state and district authorities. The research team noted a 150% surge in published health events captured since adopting AI-assisted surveillance, compared to earlier years of fully manual analysis. In 2024 alone, 96% of reported events were identified through the AI system, with only 4% coming from manual review. Globally, event-based surveillance techniques that incorporate online media or social media sources are increasingly used to complement traditional “passive reporting” from healthcare providers. The volume of daily online content, however, has made manual screening impractical, making automated systems essential. The article also references other Indian studies highlighting the value of enhanced surveillance. A pilot conducted in six private hospitals in Kasaragod, Kerala, used an algorithm to analyse cases of acute febrile illness (AFI). The system detected 88 clusters, with several verified as outbreaks—including dengue and COVID-19—demonstrating the benefits of early, data-driven detection. International research supports similar conclusions. A 2020 review in the Journal of Biomedical Informatics found that machine learning–based analysis of social media posts, especially on Twitter, improved disease trend prediction. Another study, published in 2017 in the American Journal of Tropical Medicine and Hygiene, showed that mining news articles can help fill gaps when official national case data is delayed. Overall, the findings underscore the growing importance of AI-driven surveillance systems in strengthening public health response capabilities and improving global health security. Source: PTI