Under the scalpel of AI: India’s healthcare year in 2025

If 2025 had a defining theme for India’s healthcare system, it was quiet transformation under strain. Hospitals remained crowded, doctors overstretched and patients frustrated by costs and access. Yet running through these familiar challenges was something new. Artificial intelligence steadily moving from pilot projects into everyday medical practice.

This wasn’t a year of dramatic overhauls.

Instead, 2025 showed how India’s healthcare system is slowly being reshaped, unevenly, imperfectly, but unmistakably by AI.

A system still streched thin

India began 2025 with its long-standing healthcare gaps firmly intact.

Public hospitals, especially in big cities, struggled with
overwhelming patient loads. Rural districts continued to face shortages of specialists, forcing patients to travel hours for scans, tests or consultations.

Out-of-pocket expenses remained high despite wider insurance coverage, and a serious illness could still wipe out family savings. Non-communicable diseases, read:
diabetes,
heart disease, and
cancer kept rising, demanding long-term care from a system built largely for short-term treatment.

Mental health also stayed in focus.
Demand for counselling and psychiatric care surged, but the number of trained professionals failed to keep pace, particularly outside metros.

AI enters the diagnostic room

Where 2025 marked a clear shift was diagnostics. AI-powered software moved decisively from trials to routine use, particularly in imaging.

One of the most prominent examples was
Qure.ai, an Indian startup whose algorithms read chest X-rays and CT scans to detect tuberculosis, lung cancer and brain haemorrhages. Its tools were deployed across several government TB screening programmes, helping district hospitals flag high-risk cases even when radiologists were unavailable.

In breast cancer screening,
Niramai gained ground by offering a non-invasive alternative to mammography. Using thermal imaging and AI analysis, its technology was used in camps and smaller hospitals, making early detection possible in low-resource settings where conventional machines are scarce.

Large private hospitals adopted global AI platforms such as
Aidoc and
Lunit INSIGHT, which assist radiologists by highlighting potential strokes, internal bleeding or tumours on scans in real time. Doctors describe these systems as a “second reader”—not replacing expertise, but reducing fatigue-related errors during long shifts.

Telemedicine grows smarter

Telemedicine, which expanded rapidly during the pandemic years, matured further in 2025, with AI embedded at its core.

Platforms like Practo integrated AI-driven symptom triage, appointment matching and automated follow-ups, cutting waiting times for patients in smaller towns. Global tools such as
Ada Health used AI chatbots to handle first-level patient queries, allowing doctors to focus on complex cases during virtual consultations.

For many patients, especially in semi-urban and rural areas, this meant quicker guidance without long queues. But clinicians also warned that AI symptom checkers must be carefully supervised, especially when algorithms trained on urban populations are used more broadly.

AI behind the scenes: planning and surveillance

AI’s influence extended beyond clinics into public health planning.

Health authorities increasingly used predictive analytics to anticipate disease outbreaks and hospital demand. During seasonal spikes in dengue and influenza-like illnesses, AI models helped forecast bed shortages and medicine needs.

Globally used systems such as
BlueDot, which analyses data from travel patterns, climate and health reports, informed early warning efforts. Hospitals experimented with predictive tools like KenSci to identify high-risk patients and optimise staffing and capacity.

These systems did not eliminate shortages, but they gave administrators something new: data-driven foresight. The challenge remained uneven data quality across states and districts.

Private sector moves faster than the public system

India’s private healthcare sector continued to lead AI adoption. Corporate hospital chains invested heavily in AI-assisted radiology, pathology and patient management, often marketing these tools as hallmarks of premium care.

Health-tech startups flourished, offering AI-driven pathology platforms, as well as chronic disease management tools like
HealthPlix, which uses AI-enabled electronic medical records to track patients over time.

Investors remained optimistic, seeing AI as the key to scaling healthcare in a country with limited doctors and massive demand. Critics, however, warned of a growing digital divide, where advanced tools cluster in urban, high-end hospitals.

Ethics, privacy and accountability concerns

As AI spread, concerns followed close behind.

Patient data privacy emerged as a central issue, with vast datasets being used to train algorithms. While India’s digital health architecture progressed, regulation struggled to keep pace with innovation.

Doctors raised questions about accountability. If AI software misses a diagnosis or flags a false alarm, who bears responsibility, the clinician, the hospital or the technology provider?

Bias was another worry. Algorithms trained on narrow datasets risked underperforming for women, children or marginalised communities, a serious concern in a diverse country like India.

The human element still matters

Perhaps the clearest lesson of 2025 was that technology alone cannot fix healthcare.

AI improved speed and scale, but its impact depended heavily on trained staff, functional infrastructure and trust. In some settings, lack of training meant AI tools added complexity rather than easing workloads.

Healthcare workers needed time and support to adapt, and patients needed reassurance that machines were assisting, and not replacing human judgement.