India’s One Health strategy stands at a pivotal juncture where artificial intelligence could transform fragmented surveillance into a unified shield against emerging threats.
India is at a critical crossroads in public health. With its vast human population, extensive livestock sector and rich biodiversity, the country faces heightened risks from zoonotic diseases, climate-induced outbreaks and ecological disruptions. The Observer Research Foundation (ORF) has highlighted how artificial intelligence (AI) can play a transformative role in operationalising the One Health approach – an integrated framework that recognises the unbreakable links between human, animal and environmental well-being.
In an expert analysis published by the ORF, Associate Fellow Basu Chandola argues that AI offers powerful tools for early detection, predictive modelling and coordinated response, yet its success hinges not on technological sophistication alone but on institutional reforms. “Artificial intelligence can strengthen the One Health approach in India, but its impact will depend on integrating fragmented health, veterinary, and environmental surveillance systems,” Chandola writes. The analytical piece underscores the urgency of data-driven collaboration in an era of shared risk.
The One Health Imperative in a Post-Pandemic World
The COVID-19 pandemic exposed the dangers of siloed health systems. Zoonotic spillovers – diseases jumping from animals to humans – account for a majority of emerging infectious threats. One Health, endorsed globally, demands coordinated governance across ministries handling human medicine, veterinary services, agriculture and environment. In India, dense human-animal interfaces, rapid urbanisation and climate variability amplify these risks. The Observer Research Foundation notes that without integrated surveillance, early warning signals remain invisible until outbreaks escalate. AI emerges as the missing link, capable of analysing vast, multi-sectoral datasets in real time to forecast threats before they materialise.
AI’s Transformative Role in Predictive Epidemiology and Biosurveillance
Artificial intelligence excels at pattern recognition across disparate sources. Chandola explains that machine learning models can process environmental data, climate patterns, land-use changes and animal movement to predict zoonotic hotspots. Computer vision tools already detect plant diseases in crops, analyse veterinary imaging for livestock ailments and support human diagnostics – creating a seamless monitoring network spanning agriculture, animal husbandry and public health.
Integrated biosurveillance platforms powered by AI could scan hospital records, livestock databases, wildlife networks and environmental sensors simultaneously, flagging anomalies instantly. The ORF expert emphasises that such systems would enable proactive rather than reactive responses, potentially averting pandemics at their source. For a country like India, where seasonal monsoons, deforestation and wildlife encroachment intersect daily with human activity, these predictive capabilities represent a strategic advantage.
India’s National One Health Mission and Emerging AI Initiatives
India has taken concrete steps to embed One Health into national policy. The National One Health Mission (NOHM), steered by the Office of the Principal Scientific Adviser, aims to foster coordination among ministries and build integrated surveillance infrastructure. A key report from the office identifies data analytics and artificial intelligence as critical enablers for enhancing precision and efficiency.
Several AI-driven projects are already underway. The Ministry of Health and Family Welfare deploys tools such as the Clinical Decision Support System via eSanjeevani telemedicine, AI-assisted TB screening and diabetic retinopathy detection. Centres of Excellence for AI in Health have been established at AIIMS New Delhi, PGIMER Chandigarh and AIIMS Rishikesh. On the animal health front, the Indian Council of Agricultural Research’s National Animal Disease Referral Expert System (NADRES v2) uses machine learning and geospatial analytics to forecast livestock disease outbreaks. The Indian Council of Medical Research has also invited expressions of interest for AI tools capable of detecting emerging pathogens across human, animal and environmental interfaces.
These initiatives, according to the Observer Research Foundation, form the backbone of a potential national biosurveillance architecture. Basu Chandola notes that while the ambition is clear, an implementation gap remains between policy vision and ground-level execution.
Structural Challenges Blocking AI Integration
Despite promising starts, significant hurdles persist. Data fragmentation remains the foremost obstacle: human health records, veterinary datasets, agricultural systems and environmental platforms operate in isolation across different ministries, often with incompatible formats and limited interoperability. Institutional silos between health, agriculture, animal husbandry, environment and wildlife departments further complicate coordination, even as the NOHM provides a formal platform.
Uneven public health infrastructure compounds the problem. Gaps in ground-level reporting, veterinary laboratory capacity and real-time environmental monitoring mean AI systems risk analysing incomplete or poor-quality data. Chandola warns that without addressing these foundational issues, AI could become merely “another technological layer applied to an otherwise fragmented system.”
Roadmap for Success: Governance, Infrastructure and Expertise
To harness AI’s full potential, the ORF recommends four priority actions. First, develop interoperable data systems linking human, animal and environmental databases across central and state governments. Second, strengthen biosurveillance infrastructure by expanding veterinary networks, wildlife monitoring stations and environmental sensors. Third, cultivate interdisciplinary expertise combining epidemiology, veterinary science, environmental studies and data science. Finally, establish robust AI governance frameworks that balance public health benefits with privacy protections, transparency and accountability.
Chandola concludes that India’s scale, biodiversity and dense human-animal interface present a unique opportunity. “The success of India’s One Health strategy will therefore depend less on artificial intelligence itself and more on the governance architecture built around it,” he asserts.
