Artificial Intelligence was once positioned as a game-changer in healthcare, set to transform everything from diagnostics to patient outcomes. In countries like India, with vast populations and resource-strapped systems, the potential of AI to lighten the load on overburdened healthcare workers seemed particularly promising. Yet, despite early enthusiasm, many AI-driven analytics projects in Indian hospitals have failed to scale or deliver measurable impact.
Even as prominent hospital chains like Apollo invest in AI to reduce staff workloads and boost productivity, the broader adoption of AI in Indian healthcare remains slow and uneven. So, what’s holding back this much-hyped transformation?
“AI and data analytics are likely to change the future of healthcare in India and help in improving the diagnostics, workflows and patient care,” says Ankit Shrivastava, Founder & Managing Partner of Enventure. “Although hospitals like Apollo are using AI to lighten the burden on staff and improve productivity, the adoption is still very low due to data fragmentation, regulatory risk, and incompatibility.”
Fragmented Systems, Siloed Data
One of the most significant roadblocks is the fragmentation of data across public and private healthcare institutions. Many facilities still rely on paper records, and even when electronic health records (EHRs) are used, they often exist in isolated systems that don’t communicate with each other. This lack of interoperability makes it nearly impossible to use AI effectively at scale, since the technology relies heavily on large, clean, and consistent datasets to deliver actionable insights.
A 2023 report from McKinsey notes that nearly 60% of AI applications in healthcare globally fail to progress beyond the pilot phase—largely due to data governance issues and unclear ROI. In India, this figure may be even higher due to persistent gaps in digital health infrastructure.
Shrivastava points to national initiatives such as the Ayushman Bharat Digital Mission (ABDM), the National Digital Health Blueprint (NDHB), and the Unified Health Interface (UHI) as key to solving this problem. “India has established the necessary digital infrastructure… to ensure interoperability of electronic health records,” he says. These frameworks aim to create a unified and secure system for sharing patient data across healthcare providers, which is a prerequisite for any successful AI deployment.
Regulatory Hurdles and Trust Deficit
Yet, even with technical infrastructure in place, adoption is hampered by regulatory and cultural challenges. Concerns about data privacy and cybersecurity remain front and center. India lacks comprehensive data protection laws tailored specifically for healthcare, making hospitals—and patients—wary of embracing AI-powered tools that rely on sensitive medical information.
A study published in The Lancet Regional Health – Southeast Asia in 2022 highlighted the widespread apprehension among Indian doctors toward AI, noting fears of deskilling, misdiagnosis, and increased liability. This skepticism, coupled with high implementation costs and unclear accountability frameworks, continues to dampen enthusiasm in many corners of the healthcare system.
“Nevertheless, issues such as the privacy of data, costs of implementation and skepticism from the medical community are still present to affect the effectiveness of AI,” Shrivastava says. “For the scale-up of AI to be effective in India, the country has to improve the frameworks for sharing of data, ensure that AI is embedded in ABDM and UHI, and increase the cooperation between policymakers, technology vendors and healthcare facilities.”
A Slow but Strategic Path Forward
Despite these challenges, experts remain cautiously optimistic. India’s government has signaled strong support for digital health, and collaborations between the public and private sectors are beginning to take shape. Apollo’s ongoing AI investments, for instance, could serve as a model for smaller healthcare systems looking to modernize incrementally.
Internationally, countries like Singapore have demonstrated that with strong government backing and strict data standards, AI can deliver meaningful outcomes in areas like early cancer detection and hospital readmission reduction. If India can continue building its digital foundation while addressing policy gaps, it could still fulfill AI’s promise in healthcare—just on a slower, more deliberate timeline.
For now, the AI revolution in Indian healthcare is less of a sprint and more of a marathon. But with strategic alignment, it’s a race that can still be won.