New Delhi: AI adoption in Indian healthcare remains at an early stage, but rapidly advancing capabilities and improving digital infrastructure could help hospitals move from pilots to scaled deployment, according to a new report by Bain & Company and HealthQuad.
The report, AI in Indian Healthcare Delivery, said India is better positioned to scale healthcare AI than it was during earlier waves of digital adoption, supported by government initiatives, rising electronic medical record (EMR) penetration, private capital, a growing startup ecosystem and increasing clinician acceptance.
However, adoption remains uneven. Most hospitals are still testing AI in controlled settings, with meaningful scale largely limited to operational and workflow applications. Only a handful of providers have begun expanding into clinical use cases, the report said.
The gap comes as AI capabilities are advancing rapidly. The amount of expert-level work AI can complete autonomously has been doubling every six to nine months since 2023, according to the report. Frontier models now match or outperform pre-licensed medical professionals in some controlled clinical reasoning tests, while the cost of using frontier AI models has fallen by around 92% since 2023.
For healthcare providers, this could allow AI to move beyond efficiency gains and help address one of India’s biggest healthcare constraints — limited clinician capacity. By automating administrative and repetitive tasks, AI could free doctors, nurses and other healthcare professionals to focus more on patient care and higher-value clinical work.
“AI adoption in Indian healthcare is still early, but the conditions for it to scale are strengthening quickly,” said Dhruv Sukhrani, head of Bain & Company’s Healthcare & Life Sciences practice in India.
The challenge, he said, is increasingly one of business transformation rather than technology alone, requiring hospitals to redesign workflows, build organisational capabilities and establish trust among doctors and nurses.
Data readiness remains a major hurdle. EMR adoption in India is around 35%, well below the US and UK, and remains concentrated among larger urban hospital chains. Many small and mid-sized hospitals continue to rely heavily on paper records.
The report identified data readiness, regulatory clarity and locally applied talent as key factors that will determine the pace of AI adoption. India’s regulatory framework for adaptive and autonomous clinical AI is still evolving, particularly around accountability, data governance and clinical validation.
The next opportunity could lie in moving AI from operational applications into connected patient care. The report identified remote patient monitoring, operating theatre and ICU optimisation, and post-discharge chronic disease management as areas with significant headroom.
For hospitals, the report said AI needs to be treated as a business transformation rather than an IT project, with adoption tied to clinical outcomes and supported by appropriate governance and human oversight.
Ultimately, scaling AI in Indian healthcare will depend on bringing together value, deployability and trust, while building the data, workflow and clinical foundations needed for wider adoption.
