This essay is part of a series. World Health Day 2026: Standing with science in an era of shared risk
The World Health Day 2026 theme, “Standing together for health, standing with science,” emphasizes the importance of scientific collaboration in protecting the health of people, animals, plants, and ecosystems through a “One Health” approach. In an era defined by zoonotic disease spillover, climate-driven disease change, and ecosystem destruction, artificial intelligence (AI) provides a powerful tool to advance this challenge by enabling early detection of emerging health threats. In a country like India, where population density coexists with large livestock systems and rich biodiversity, operationalizing the One Health approach through data-driven systems is especially important. By analyzing large and diverse datasets, AI can improve predictive epidemiology, enhance biosurveillance, and support a more coordinated public health response. This article examines how AI can help operationalize the One Health agenda and assesses India’s evolving efforts to integrate AI into the public health ecosystem.
AI and One Health Framework
Simply put, One Health is an integrated framework that recognizes the interconnectedness of human, animal, and environmental health and calls for coordinated governance across these areas. This approach has taken on new urgency in the wake of the COVID-19 pandemic, which has exposed the limitations of fragmented disease surveillance systems. AI is central to driving this agenda, as its diverse applications enable more integrated, data-driven health surveillance and response systems. In healthcare systems, AI is already supporting diagnosis, clinical decision-making, patient monitoring, and hospital management. But the possibilities extend beyond traditional medicine and are particularly relevant to operationalizing the One Health agenda.
AI can also enhance predictive epidemiology by analyzing environmental and ecological data such as climate patterns, land use changes, and animal movements to predict where zoonotic disease spillover is most likely to occur.
AI enables integrated biological surveillance by analyzing data from hospitals, livestock monitoring systems, wildlife monitoring networks, and environmental sensors. By identifying anomalous patterns across these datasets, machine learning models can provide early warning of new outbreaks that traditional surveillance systems may miss. AI can also enhance predictive epidemiology by analyzing environmental and ecological data such as climate patterns, land use changes, and animal movements to predict where zoonotic disease spillover is most likely to occur.
Additionally, computer vision systems are increasingly used to detect plant diseases, analyze veterinary images, and support clinical diagnosis in human medicine. Such tools could enable more integrated disease monitoring across agriculture, livestock systems, and public health networks. Finally, AI can support policy coordination by enabling integrated data platforms that enable faster information sharing between agencies responsible for health, agriculture, livestock, and environmental protection.
New initiatives in India
India has recently started incorporating the One Health approach into its health governance architecture. The National One Health Mission (NOHM) aims to strengthen collaboration between human, animal, and environmental health agencies to improve pandemic preparedness through integrated disease surveillance, cross-sectoral data sharing, and coordinated outbreak response. AI is increasingly integrated into this new ecosystem. The Office of the Chief Scientific Adviser’s report on ‘Engagement Workshop for States and Union Territories under the National One Health Mission’ identifies data analytics and artificial intelligence as key enablers to improve surveillance accuracy and operational efficiency within the framework of One Health.
Several AI-powered initiatives have already been implemented across India’s public health system. The Ministry of Health and Family Welfare (MoHFW) has introduced AI tools for disease surveillance, telemedicine, and national disease control programmes. These include a clinical decision support system integrated with the eSanjeevani telemedicine platform, a media disease surveillance system based on the Integrated Disease Surveillance Program, and AI-enabled tools to support tuberculosis screening and prediction of adverse tuberculosis outcomes. AI-based diagnostic tools, such as diabetic retinopathy screening, are also being introduced to improve early detection and clinical decision-making. Centers of Excellence for AI in Health have been established at All India Institute of Medical Sciences (AIIMS), New Delhi, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, and AIIMS Rishikesh to support the development and implementation of such technologies.
The Office of the Chief Scientific Adviser’s report on ‘Engagement Workshop for States and Union Territories under the National One Health Mission’ identifies data analytics and artificial intelligence as key enablers to improve surveillance accuracy and operational efficiency within the framework of One Health.
Similar efforts are being made in animal health monitoring. The National Animal Disease Query Expert System (NADRES v2), developed by the Indian Council of Agricultural Research (ICAR) – National Institute of Veterinary Epidemiology and Disease Informatics, uses artificial intelligence, machine learning, and geospatial analysis to predict outbreaks of livestock diseases across India. Recently, the Indian Council of Medical Research (ICMR) invited expressions of interest under NOHM for AI-enabled tools to support early detection of emerging pathogens across human, animal, and environmental systems.
Taken together, these efforts demonstrate the emergence of an AI-powered biosurveillance ecosystem. If these systems can be effectively integrated, they could form the backbone of a national biosurveillance architecture that can detect signals across health systems, livestock surveillance networks, and environmental databases. However, there remains a wide gap between ambition and implementation.
structural challenges
The biggest challenge facing India’s AI-enabled One Health system is data fragmentation. Human health records, veterinary surveillance datasets, agricultural information systems, and environmental monitoring platforms span multiple ministries. These systems often operate on different standards and rarely interoperate. Without integrated datasets, AI systems cannot generate meaningful insights across sectors. Fragmentation of the system further complicates the situation. Effective implementation of the One Health approach requires coordination between ministries responsible for health, agriculture, livestock, environment, and wildlife conservation. Although NOHM provides a coordination platform, bureaucratic silos remain deeply entrenched.
Effective implementation of the One Health approach requires coordination between ministries responsible for health, agriculture, livestock, environment, and wildlife conservation. Although NOHM provides a coordination platform, bureaucratic silos remain deeply entrenched.
Additionally, uneven public health capacity remains a constraint. Effective biological surveillance relies not only on advanced analysis but also on reliable ground-level reporting systems. Many regions continue to face shortages in disease surveillance infrastructure, veterinary services, and testing capacity. These structural limitations mean that AI tools, if deployed alone, risk being technologically overlaid on fragmented healthcare governance systems.
From vision to system
If India wants to leverage AI effectively within the framework of One Health, it needs to focus on the following policy priorities.
- Interoperable data systems must connect human, animal, and environmental health databases across departments and states.
- Generating reliable data for AI systems requires strengthening biosurveillance infrastructure, such as veterinary networks, wildlife monitoring systems, and environmental sensors.
- Building multidisciplinary expertise across epidemiology, veterinary medicine, environmental science, and data science is essential.
- Ensuring that AI-powered surveillance balances public health goals with privacy, transparency, and accountability requires a robust governance framework.
AI provides a powerful tool for operationalizing the One Health approach by integrating signals from human health systems, veterinary networks, and environmental monitoring platforms to detect and respond to emerging health threats. For India, the opportunity is considerable given the country’s size, biodiversity and close human-animal interface. However, the real challenge lies not in the sophistication of AI models, but in the institutional reforms needed to support them. Without interoperable data systems, cross-sectoral coordination, and continued investment in public health capacity, AI risks becoming another layer of technology applied to a fragmented system. The success of India’s One Health strategy will therefore depend more on the governance architecture built around artificial intelligence than on artificial intelligence itself.
Basu Chandra He is an Associate Fellow at the Center for Digital Society at the Observer Research Foundation.
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