Last week, New Delhi buzzed with the AI Impact Summit, promising real-world results over abstract discussions. Yet, the robo-dog spectacle at the summit stole the spotlight for all the wrong reasons, reminding us that India needs less theatrics and more substance. For India, the real issue is not what impresses on stage, but what actually saves lives and livelihoods.
Can Artificial Intelligence (AI) step in to prevent the wave of deaths and diseases looming over the next few months?
The heat season is already tightening its grip across much of the country and will intensify from March to early June. This will be followed by the monsoon rains that unleash dengue and other mosquito- and water-borne diseases. Year after year, these dangers return, and the climate crisis is making it worse. Though we have enough warnings, we simply lack the readiness to tackle these disasters that are obviously waiting in front of us.
Disaster management should not wait for a disaster to strike. It should start well in advance through proactive planning, targeted forecasts, and swift local action. Despite the rollout of climate and heat action plans, too many municipalities still treat heat and dengue like surprise visitors each year, struggling to respond only after the damage is done. Water tankers roll out after the first deaths, fogging starts after cases surge, and hospitals rush to catch up when they should be staying ahead. This is not a strategy or informed action — it is a desperate reaction.
So, what should an AI-enabled early warning system for climate and health look like in India?
The answer starts with heat because it is our most urgent and deadly threat. While some cities have heat action plans, improved public messaging, cooling centers, and access to drinking water, heat deaths keep climbing. This is because heat has become an annual affair instead of an occasional crisis.
AI and machine learning have the potential to transform weather forecasts into daily, hyper-local health risk alerts. This involves analyzing temperature, humidity, night-time heat, and urban hotspots, while considering the most vulnerable, including the elderly, those without adequate housing, outdoor workers, and populations without access to cooling. The system should then trigger clear actions tailored to each city. This should include ensuring emergency rooms are fully staffed, that supplies like ORS and IV fluids are stocked, and that cooling shelters are open before emergencies start. Work-rest rules should be enforced, not just suggested in guidelines.
We have the tools to predict climate-driven diseases by training models with past climate and health data, but this needs reliable health data. Right now, our health and death records are scattered and often underreported, and even researchers struggle to access them. Without reliable, up-to-date health data, even the best forecasts are just another set of maps and numbers. To build predictive systems for heat and health, we need to treat public health data as critical national infrastructure.
A recent project we worked on shows how we can forecast dengue risk using climate data. Across India, dengue cases are highest during the monsoon season, especially from June to September. But the conditions for high dengue often start earlier. Research led by my PhD student found that more dengue cases are linked to temperatures, rainfall, and humidity from March to May, as well as rainfall during the monsoon. This means we can use pre-monsoon conditions to predict dengue spikes before the monsoon arrives.
This research also reveals how dengue risk could rise in the near future as the climate crisis accelerates. Here, AI can save time, money, and lives. AI can combine seasonal climate forecasts, local weather data, satellite imagery of land and water, and disease-tracking data to produce dengue risk forecasts. It can help target mosquito control in the right neighborhoods and warn communities before outbreaks. If used well, AI can guide health departments on where to send tests, platelets, and staff where they are needed most, before the crisis peaks.
India can improve, but only if we treat this as essential public infrastructure, not another flashy product for an AI summit. First, we must weave climate-health early warning systems into the fabric of our public infrastructure. This means setting up open data links between the India Meteorological Department, health agencies, and city systems. Most importantly, we need to share real results, not just launch new dashboards.
Second, people must remain at the heart of the process. AI should empower, not replace, human judgment in critical decisions. Public officials need to review and approve AI-generated outputs, always prioritizing safety and fairness. In health, this means doctors, disease experts, and city leaders should make the final decisions, not a computer model.
Ultimately, we must measure the real-world impact. Did hospital admissions for heat actually fall in areas where early warnings were used? Did dengue cases go down where pre-monsoon actions were taken based on forecasts? If we cannot answer these questions, then our system is just another performance at another summit.
The heat has already arrived, and soon dengue, chikungunya, and other mosquito- and water-borne diseases will follow close behind. Global climate agencies indicate a chance of an El Niño event emerging in the latter half of 2026, a reminder that heat and weather swings may be sharper than usual. Taking precautions now is cheaper and easier than rushing to respond in an emergency later. While the world debates the future and promises of AI, India can show that real impact means fewer people collapsing in the heat and fewer children suffering in packed hospital wards.
Roxy Mathew Koll is a climate scientist at the Indian Institute of Tropical Meteorology and a lead author of recent IPCC reports. The views expressed are personal
