Pune-based healthcare AI startup Froncort.AI is expanding its disease surveillance technology with Google.org backing, aiming to help frontline health workers identify disease hotspots, target vulnerable communities and improve screening efficiency across India.
India’s fight against infectious diseases is increasingly turning to artificial intelligence, with healthcare technology emerging as a potential game-changer in early detection and targeted screening. Adding momentum to this shift, Pune-based healthcare AI startup Froncort.AI has secured $2.3 million in funding from Google.org to expand its AI-powered disease screening capabilities and strengthen public health programmes across the country.
Alongside the funding, the company will receive $200,000 in Google Cloud credits and pro bono technical support from Google. The investment will accelerate the development of a mobile application designed to bring advanced disease intelligence directly to frontline health workers, particularly in communities where access to healthcare resources and specialist expertise remains limited.
Google.org Backs AI Innovation for Public Healthcare
Froncort.AI is among 15 organisations selected for the Google.org Impact Challenge: AI for Government Innovation, an initiative supporting the use of artificial intelligence to address public-sector challenges. The latest funding also builds on Google’s earlier support for the company’s AI-NETRA validation study.
At the centre of this initiative is mAI-NETRA, a mobile-first version of Froncort.AI’s existing AI-NETRA platform. The solution is being developed to help frontline healthcare workers identify communities at greater risk of infectious diseases and determine where household-level screening efforts should be prioritised.
Explaining the thinking behind the technology, Dr Ujjwal Rao, Founder and CEO of Froncort.AI, highlighted the gap between available medical expertise and its accessibility at the community level.
“The world does not lack experts. It lacks their reach. AI-NETRA puts that expertise in the health worker’s hands at the door: where to go, whom to screen, and when,” Rao said.
The approach reflects a broader opportunity for AI in public healthcare: enabling existing health workers to make more informed decisions rather than relying entirely on conventional, resource-intensive screening methods.
How AI-NETRA Uses Data to Identify Disease Hotspots
The upcoming mAI-NETRA application will combine government health programme information with Google’s Earth AI models and additional geographical and environmental indicators to identify areas with elevated disease risks.
Its analysis will incorporate several local factors, including:
- Settlement density and population distribution.
- Land-use patterns and geographical characteristics.
- Rainfall and seasonal environmental conditions.
- Flooding patterns and other factors associated with disease transmission.
- Government health programme data and local disease indicators.
Using these inputs, the platform identifies high-risk locations within 300-metre zones. It then converts the findings into prioritised work plans, household clusters and optimised routes for frontline health workers.
Rather than following broad screening schedules, healthcare teams can use these AI-generated insights to focus their efforts on communities where disease detection is more likely.
An important feature of mAI-NETRA is its ability to operate offline and support local languages. This is particularly relevant for healthcare workers operating in remote and underserved areas, where internet connectivity may be unreliable.
The application will allow workers to access screening priorities and household routes directly through their mobile phones. Froncort.AI has also stated that government health programme data will be stored and processed locally within India, addressing important data security and sovereignty considerations.
Pilot Study Demonstrates the Potential of AI-Guided Screening
The funding announcement comes after promising results from a validation study conducted in collaboration with Maharashtra’s state tuberculosis programme.
According to Froncort.AI, AI-guided screening teams identified a comparable number of confirmed pulmonary tuberculosis cases while examining significantly fewer people than conventional screening teams.
The results suggest that AI-guided screening could substantially improve the efficiency of identifying tuberculosis cases. While the AI-supported teams screened approximately 63% fewer people, they detected 10 confirmed cases compared with nine through standard screening.
The company also reported a significant reduction in screening costs, with the cost per confirmed case falling from approximately $29,000 to $10,000. Furthermore, 92% of participating frontline health workers reported experiencing a lower workload.
These findings highlight how data-driven targeting could help public health authorities use limited resources more effectively while reducing the operational pressure on healthcare teams.
Six Infectious Diseases in Focus as Deployment Expands
With the latest Google.org funding, Froncort.AI intends to expand mAI-NETRA beyond tuberculosis screening to cover six infectious diseases:
- Tuberculosis (TB)
- Leprosy
- Malaria
- Dengue
- Cholera
- Leptospirosis
The company plans to deploy the technology across up to eight high-burden districts in three Indian states, with an estimated coverage of two to three million people.
This expansion could provide public health authorities with a more comprehensive approach to identifying disease-prone communities and planning preventive interventions. By bringing multiple disease indicators into one platform, the technology aims to support more coordinated screening and surveillance activities.
The deployment will also be independently monitored against five performance objectives. These include identifying at least 20% more cases, reducing the number of people screened per confirmed case by more than 50%, accelerating sample transportation, lowering government expenditure per detected case and improving performance-based earnings for frontline health workers.
The targets are intended to measure not just the platform’s technological capabilities but also its practical contribution to healthcare delivery.
Froncort.AI Eyes an Open-Source Future for Public Health AI
Beyond its initial deployment plans, Froncort.AI is looking to make its technology accessible to a wider public health ecosystem. The company intends to publish mAI-NETRA as an open-source blueprint, enabling other governments to adopt, customise and integrate the approach into their own healthcare programmes.
Such an approach could extend the potential impact of the platform beyond the districts initially targeted in India. Governments and public health institutions could adapt the underlying framework to local disease patterns, geographical conditions and healthcare infrastructure.
The initiative also reflects the growing role of artificial intelligence in strengthening India’s public healthcare infrastructure. While AI has already made inroads into medical imaging, diagnostics and clinical decision support, its application in identifying disease hotspots and improving the deployment of healthcare workers represents another important area of development.
With Google.org’s financial and technical backing, Froncort.AI is positioning mAI-NETRA as a technology-led approach to making disease screening more targeted, efficient and accessible.
Ultimately, the success of the initiative will depend on its ability to translate predictive insights into measurable improvements on the ground. If its upcoming deployments meet their targets, the platform could offer a scalable model for helping governments detect infectious diseases earlier, reach underserved populations and make better use of limited public healthcare resources.
Read more:Â Top 20 SaaS Infrastructure Leaders to Watch in the USA
