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NEW DELHI — In a significant leap toward digital health transformation, the Government of India has formally integrated artificial intelligence (AI) into its national public health infrastructure to combat two of the country’s most persistent health challenges: tuberculosis (TB) and diabetes.
Union Minister of State for Health and Family Welfare, Prataprao Jadhav, confirmed in Parliament this week that the Health Ministry has deployed specific AI-based diagnostic solutions—including “Cough Against TB” and “MadhuNetrAI”—across multiple states. The initiative marks a strategic shift from conventional screening to algorithmic diagnostics, aiming to bridge the critical gap in healthcare access for India’s 1.4 billion citizens.
The AI Offensive: Identifying the “Missing Millions”
The cornerstone of this initiative is the deployment of Cough Against TB (CATB), an AI-powered solution developed to screen for pulmonary tuberculosis. According to government data presented in the Rajya Sabha, the tool has already screened over 1.62 lakh individuals in community settings between March 2023 and November 2025.
“In the deployed geographies, the solution has shown an additional yield of 12-16 percent in TB reported over patient screenings using conventional methods,” Minister Jadhav stated.
This “additional yield” is crucial for India’s National TB Elimination Programme (NTEP). Despite a 21% decline in TB incidence since 2015, India still bears approximately 25% of the global TB burden. One of the program’s biggest hurdles has been the “missing millions”—undiagnosed carriers who unknowingly spread the infection. CATB, which utilizes sound analysis algorithms to detect TB-specific patterns in a patient’s cough, offers a non-invasive, rapid triage tool that can function in remote areas without immediate access to X-ray machines or sputum testing labs.
Vision for the Future: Tackling the Diabetes Epidemic
Parallel to the TB initiative, the government is tackling the complications of India’s “diabetes epidemic” with MadhuNetrAI. Developed in collaboration with AIIMS Delhi and Wadhwani AI, this tool automates the detection of Diabetic Retinopathy (DR)—a leading cause of blindness among diabetics—from retinal fundus images.
The scale of the problem is immense. A recent ICMR-INDIAB study estimates that over 101 million Indians live with diabetes. However, less than 10% of diabetic patients in India undergo regular eye screenings, largely due to a shortage of ophthalmologists in rural areas.
MadhuNetrAI addresses this by allowing non-specialist health workers to capture retinal images, which the AI then analyzes to flag patients requiring urgent specialist care. “The solution has been implemented across 38 facilities in 11 states, assisting in the screening of more than 14,000 retinal images and benefiting 7,100 patients,” the Ministry reported. By triaging patients effectively, the system optimizes scarce specialist resources for those with moderate-to-severe retinopathy.
Expert Perspectives on Algorithmic Care
Medical experts have cautiously welcomed the move, highlighting the potential for AI to democratize quality healthcare.
“The biggest barrier to preventing blindness in diabetes is access,” explains Dr. Rohan Chawla, a retina specialist at AIIMS involved in the validation of such tools. “Most AI models are tested on small datasets, but tools like MadhuNetrAI have undergone validation on thousands of images to ensure clinical confidence. If we can filter out the normal cases using AI, ophthalmologists can focus entirely on saving the sight of those who are actually at risk.”
However, experts also warn that AI is a tool, not a replacement for clinicians. Dr. Anant Bhan, a researcher in global health and bioethics, notes, “While AI offers tremendous speed, the ‘human in the loop’ remains essential. We must ensure that false positives do not overburden our referral centers, and false negatives do not give patients a wrong sense of security.”
Infrastructure and Governance
To support this digital backbone, the Health Ministry has designated three premier institutions—AIIMS Delhi, PGIMER Chandigarh, and AIIMS Rishikesh—as ‘Centres of Excellence (CoE) for Artificial Intelligence.’ These centers will spearhead the development, validation, and ethical deployment of future AI solutions.
Furthermore, the government emphasized adherence to the MeitY AI Governance Guidelines and ICMR’s Ethical Guidelines for Application of AI in Biomedical Research. This regulatory framework is intended to address growing concerns regarding patient data privacy and algorithmic bias—ensuring that AI tools perform equally well across India’s diverse demographic profiles.
Implications for Public Health
For the average citizen, this technological upgrade means faster, more accessible diagnosis.
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For TB: Residents in rural areas may no longer need to travel miles for an initial screening; a simple audio analysis at a local wellness center could trigger the necessary follow-up.
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For Diabetes: Patients managing chronic high blood sugar can theoretically get their eyes checked during a routine clinic visit, potentially preventing irreversible vision loss.
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Telemedicine: The integration of an AI-based Clinical Decision Support System (CDSS) into the eSanjeevani national telemedicine platform has already supported over 282 million consultations, helping doctors across the country make standardized, evidence-based diagnoses.
Challenges and Limitations
Despite the optimism, the rollout faces logistical hurdles. Rural internet connectivity and power stability remain inconsistent, potentially hampering cloud-based AI processing. Additionally, the “black box” nature of deep learning algorithms—where the reasoning behind a diagnosis is not always transparent—raises liability questions. If an AI misses a diagnosis, who is responsible?
Moreover, while a 12-16% increase in TB detection is promising, it also requires a commensurate increase in treatment availability and follow-up infrastructure to manage the higher caseload.
Conclusion
As India races to meet its health targets, the integration of AI represents a pivotal shift from reactive to proactive healthcare. By leveraging technology to extend the reach of specialists, the government aims to ensure that geography no longer dictates the quality of care a patient receives. As these tools evolve, their success will depend not just on the code, but on the strengthening of the physical healthcare system that must support them.
Medical Disclaimer: This article is for informational purposes only and should not be considered medical advice. Always consult with qualified healthcare professionals before making any health-related decisions or changes to your treatment plan. The information presented here is based on current research and expert opinions, which may evolve as new evidence emerges.
References
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Government Source: Press Information Bureau (PIB). “Steps taken to include AI-based diagnostic tools in healthcare.” Ministry of Health and Family Welfare, Government of India. December 5, 2025.
- https://nagalandtribune.in/ai-based-diagnostic-tools-boosting-tb-diabetes-care-govt/#:~:text=Under%20the%20Tuberculosis%20elimination%20programme,patient%20screenings%20using%20conventional%20methods.
