Can India’s healthcare system keep pace with AI ambitions?

This article was first published on Express Healthcare.in on May 21 2026. Please click here to read the article.

Artificial intelligence is increasingly being seen as a catalyst for change in healthcare, both globally and in India. From supporting early diagnosis to streamlining hospital operations, AI offers clear potential to improve efficiency and patient outcomes. It could also help healthcare systems respond to rising demand. Yet, as enthusiasm around AI grows, a fundamental question remains to be asked- Are healthcare systems in India truly ready to support it?

At the heart of AI lies data. These systems rely on large volumes of accurate, well-structured information to deliver meaningful insights. However, across many healthcare systems, including India’s, data availability and quality continue to present challenges. Insights from KPMG International’s 2025 Healthcare CEO Outlook highlight that while 72 per cent of healthcare CEOs believe their organisations are keeping pace with AI and 87 per cent plan to increase investment, 55 per cent identify data readiness as a major challenge. This reflects India’s situation, where interest and investment in AI are rising, but data systems are still evolving.

One of the key challenges in India is the fragmented nature of healthcare data. Patient information is often dispersed across the healthcare ecosystem, be it hospitals, clinics, laboratories, and insurers, all stored in different systems and formats. This sometimes creates barriers to accessing and using data effectively when it is needed most.

Efforts such as the Ayushman Bharat Digital Mission (ABDM) are aimed at addressing this issue by promoting digital health records and enabling data sharing. While these initiatives are encouraging and mark meaningful progress, the reality on the ground remains uneven, with varying levels of digitisation and connectivity across regions

More often than not, this lack of integration directly impacts the ability to scale AI solutions. AI tools perform best when data is consistent, standardised, and connected. Without this foundation, most implementations remain limited to pilots or narrow use cases, rather than delivering system-wide impact.

These structural gaps become more noticeable in the context of rising healthcare demand. India is experiencing population growth, a gradual increase in chronic conditions, and evolving patient expectations. At the same time, constraints such as workforce shortages and uneven infrastructure continue to pose challenges.

In this environment, improving productivity and making better use of existing resources has become essential. Digital tools and connected systems are often seen as part of the solution, but their effectiveness depends heavily on how well underlying data is integrated.

When systems do not communicate with each other, inefficiencies emerge. They could sometimes result in either duplicate tests, delays in treatment decisions, and fragmented patient journeys. Given that patients frequently move across providers, we believe connected data is key to ensuring continuity of care and better outcomes.

These foundational elements not only improve coordination and access to information but also create the conditions needed for AI to be effective at scale. At the same time, India’s healthcare system brings a unique advantage. In some areas, the relative absence of deeply embedded legacy systems offers an opportunity to leapfrog towards more modern, flexible digital solutions. With the right focus on standards, infrastructure, and participation from stakeholders, there is potential to build a more connected and future-ready ecosystem.

There is growing recognition that meaningful transformation in healthcare goes beyond adopting advanced technologies like AI. It also depends on strong digital foundations. In India, while government-led initiatives have laid important groundwork, there is an opportunity to build further momentum by accelerating the expansion of electronic health records, strengthening shared data platforms, and enhancing system-wide connectivity.