Last updated : September 13, 2026 6:00 am
For India, the opportunity is to combine technological capability with public-health experience, clinical expertise and community trust
Artificial intelligence is increasingly moving from discussion to practical use across health systems worldwide. In public health, its greatest value may not lie in replacing human expertise, but in helping health professionals and decision-makers identify risks earlier, target services more effectively and make better use of limited resources.
For India, this opportunity is particularly relevant. A large, diverse population, varied disease patterns and rapidly expanding digital-health capacity create an environment in which carefully designed AI tools can support prevention, early detection and stronger health-system planning. The central task is to ensure that innovation remains evidence-based, people-centred and suited to local needs.
The World Health Organization has noted that AI is already contributing to diagnosis, clinical care, drug development, disease surveillance, outbreak response and health-systems management. It also stresses that wider access to these innovations is important so that digital progress does not become “another driver for inequity.”
Supporting preventive public-health action
Disease surveillance is one area where AI can offer substantial public-health value. Health systems generate large volumes of information through laboratory testing, clinical services, programme records and community reporting. AI-enabled systems can assist in identifying patterns within this information, potentially helping public-health teams detect unusual trends and respond sooner.
This can be especially useful for infectious diseases influenced by seasonality, mobility, environmental conditions and climate. AI-supported analysis could complement existing surveillance by helping teams anticipate increases in vector-borne illnesses, respiratory infections or other local health priorities. Importantly, such tools work best when embedded within established public-health workflows and used alongside the experience of surveillance officers, laboratory staff and frontline health workers.
Indian academic and public-health researchers are also examining how AI can support community-level prevention. A 2025 scoping review by researchers from institutions in Tamil Nadu analysed 48 studies on the use of AI for early detection, disease prediction, treatment support and prevention in community-health settings. The review identified promising applications in malaria detection, tuberculosis screening, chronic kidney disease prediction and diabetes-risk assessment. It reported, for example, that AI- supported cough analysis for tuberculosis screening achieved an accuracy of 86% in one study, while a smartphone-based system for malaria detection from blood-smear images reported 95% accuracy.
These findings should be interpreted as evidence of potential rather than as a guarantee of performance in every setting. The authors also highlighted the importance of dataprivacy, algorithmic fairness, infrastructure and workforce training. This balanced approach is consistent with WHO’s recommendation that AI in health should promote public benefit while protecting human autonomy, safety, inclusion and equity.
Improving screening and service planning
AI may also support preventive care by helping to prioritise screening, identify people who could benefit from follow-up and assist trained professionals in reviewing health images or records. In public health, its role should be understood as supportive: the technology can help organise information and highlight potential concerns, while qualified professionals retain responsibility for interpretation, communication and action.
Resource allocation is another practical application. Health managers may use predictive tools to anticipate demand, plan outreach activities, support supply-chain decisions or identify areas where additional prevention efforts could be useful. These applications may be less visible than high-profile AI products, yet they can help direct time and services where they are most needed.
India’s developing digital-health ecosystem offers a foundation for such work. Government information released in 2026 describes AI-supported applications in areas including disease surveillance, tuberculosis screening, telemedicine decision support and diabetic-retinopathy identification, with emphasis on data anonymisation, consent and human-in-the-loop approaches.
Learning from global practice
Globally, attention is increasingly focused on how to scale AI safely and responsibly. WHO’s guidance sets out six principles for AI in health: protecting human autonomy; promoting human well-being, safety and the public interest; ensuring transparency and explainability; fostering accountability; ensuring inclusion and equity; and promoting responsive, sustainable AI.
European approaches offer one useful example of this direction of travel. The EU AI Act 2024 applies stronger safeguards to higher-risk health applications, including requirements relating to risk management, data quality, documentation, human oversight and ongoing monitoring. While every health system must develop solutions that fit its own context, the broader lesson is widely relevant: responsible innovation requires safeguards to be considered from the beginning, rather than added after a tool is deployed.
Building trust alongside innovation
Public-health AI depends on public confidence. Health data are personal, and people should be able to understand how their information is collected, protected and used.Clear governance, robust data-security arrangements, meaningful consent and transparent accountability can help sustain this trust.
Equally, AI tools need to be tested in the populations and settings in which they will be used. Health needs and service realities can differ across regions, languages, age groups and communities. Local evaluation helps ensure that technology is accurate, useful and inclusive in practice.
WHO’s approach is helpful here: AI should advance public benefit while remaining accountable to health workers and the communities affected by its use. This positions technology not as an end in itself, but as one component of a stronger health system.
A people-centred future
The next phase of AI in public health will be defined less by headlines and more by implementation. The most valuable applications may be those that help identify an emerging health risk earlier, support a frontline worker with a timely referral, improve access to screening or assist managers in planning services more effectively.
For India, the opportunity is to combine technological capability with public-health experience, clinical expertise and community trust. If AI is introduced with strong evidence, appropriate safeguards and clear human oversight, it can become a practical partner in improving population health—supporting the people and systems already working to deliver care every day.
About Author:
Dr. Vikram Niranjan is a public-health researcher, academic and health communicator whose work focuses on public health, health literacy, environmental health and equitable approaches to disease prevention. He writes on how research, policy and innovation can be translated into practical improvements in population health.
*The author’s views are his own and do not necessarily represent those of the publisher.