Inside Indian Hospitals' Multi-Billion Dollar AI Pivot

Nowadays, when a surgical oncologist wants a mid-consultation during a procedure or examination, the instinct is no longer to pull up a journal database. Doctors now increasingly rely on AI-powered knowledge platforms that retrieve verified clinical literature in seconds. 

This shift, modest as it appears, signals something larger happening across India's private hospital sector.

India’s healthcare sector is among the most AI-ready markets in the Asia-Pacific as consumers increasingly use GenAI to better understand diagnoses and treatment options, per a recent Bain & Company report.

Dr Sandeep Nayak, Chairman of Oncology and Executive Director of Surgical Oncology and Robotic Surgery at Krishna Institute of Medical Sciences (KIMS) Hospitals in Bengaluru, describes the current state of AI in healthcare as one that is "actively progressing across both clinical care delivery and administrative workflows, though it is currently in an evolving phase."

That evolution, though, is not uniform. 

Where AI Has Found Footing

AI adoption has been the farthest in diagnostics. Radiology and pathology departments, driven by the repetitive, pattern-intensive nature of their work, have been the clearest beneficiaries. According to a 2025 study by researchers in India and published in the European Journal of Cardiovascular Medicine, AI integration in internal medicine removed diagnostic errors from 22% to 12%.

According to Dr Nayak, AI in these specialities "significantly optimises workflows, reducing a physician's repetitive workload by at least 50% by automatically flagging known patterns," he tells AIM.

Many healthcare institutions have also embedded voice-to-text transcription into daily clinical reporting, with dictation tools feeding directly into electronic health records (EHR).

The rapid adoption is also inflating industry estimates. The AI in healthcare market in India is currently valued at approximately $1.6 billion and is projected to grow to over $34 billion by the mid-2030s, per Grand View Research.

At the ward level, progress is more cautious. Conversational AI tools capable of capturing patient dialogue and structuring it into EHR entries are emerging, but their rollout at scale faces a structural reality unique to India: an abundant clinical and administrative workforce that reduces the economic imperative for automation. 

Individual Adoption Outpacing Institutions

One pattern is consistent across Indian hospitals—individual clinicians are adopting AI tools faster than the institutions they work in. Doctors are using specialised platforms, including homegrown tools such as Paperguide, an AI-powered research assistant, to stay updated with clinical research. These platforms allow physicians to input complex queries and receive responses drawn from verified medical literature.

"To stay clinically updated, individual doctors are aggressively utilising specialised, Indian-developed, knowledge-based AI tools," Dr Nayak tells AIM. "These tools allow physicians to input complex research or clinical queries and receive verified, immediate updates."

This gap between individual and enterprise adoption is not unique to India. Globally, nearly two-thirds of healthcare organisations have begun integrating AI tools, according to Grand View Research, yet many remain in early-stage implementation, with proof-of-concept pilots common and full enterprise-wide integration less widespread.

The ROI Question

Measuring return on investment from AI in healthcare is a difficult undertaking. Dr Nayak argues that the question is framed incorrectly when institutions look for direct revenue generation. 

"AI tools do not directly earn revenue; instead, they generate indirect financial benefits by significantly lowering operational costs and eliminating administrative friction," he explains.

The value, in his view, lies in continuous data analysis—shifting hospital operations from end-of-month reviews to real-time monitoring, flagging anomalies, automating routine patient outreach, and identifying revenue leakages on a daily basis. 

The Data Privacy Wall

The most significant constraint on broader AI deployment is not technological capability, it is data access. Large hospital networks, including KIMS, do not allow external AI models to directly access core EHR systems. Patient data privacy and security vulnerabilities remain the primary reason.

To work around this, hospitals are turning to API-based architectures that allow AI tools to interact with data through secure, restricted interfaces without exposing underlying databases directly. Dr Nayak is candid about the gap. "The market currently lacks comprehensive, broad-spectrum enterprise products that seamlessly and securely fit into an entire hospital ecosystem without risking data leaks."

Moreover, the fragmented nature of healthcare data is another key challenge as patient information is dispersed across hospitals, clinics, laboratories, and insurers. Insights from KPMG’s 2025 Healthcare CEO Outlook highlight that while 72% of healthcare CEOs believe their organisations are keeping pace with AI and 87% plan to increase investment, 55% identify data readiness as a major challenge.

The Hallucination Problem

Perhaps the most clinically consequential limitation of current AI systems is hallucination, or their tendency to generate false or fabricated information. . A clinician survey from Cornell found that over 91% of respondents had encountered medical hallucinations from AI, and approximately 85% considered them capable of causing patient harm.

Dr Nayak highlights another risk when it comes to published medical literature: roughly 15% of healthcare citations are AI-generated, creating what he describes as "a dangerous cycle of false re-referencing." 

The response from specialised medical AI companies has been to constrain what their models can access. By anchoring large language models to verified, real-world medical databases through restricted APIs, companies such as Paperguide are working to reduce the probability of fabricated responses. 

The Irreducible Human Element

However, despite the persistent issues, medical prctitioners insist that AI cannot replace clinical judgement. "Medicine is highly subjective rather than purely objective," he says. "Virtual or algorithmic systems cannot physically examine a patient, palpate an abdomen, or interpret the nuanced physical parameters required for an accurate diagnosis."

He draws a distinction between a tool's capability and a clinician's role. An AI-enabled stethoscope can analyse heart and lung sounds against thousands of pre-fed acoustic data points. But the final clinical interpretation—the synthesis of sound, observation, history, and context—requires a physician.

India's healthcare system is navigating a decisive moment, with mounting public health pressures from chronic diseases and regional disparities that have heightened the urgency around digital tools. AI is being pulled into that space, and for good reason.