AI and the Future of Healthcare in India

India’s healthcare system is undergoing a technological shift. The promise of AI arrives into a context marked by workforce shortages, uneven regulation, fragile training systems, and vast disparities between public and private care. This makes the integration of AI not just a technological question but a deeply structural one: Who does this transformation serve, who bears the risks, and what changes when decisions about care begin to rely on opaque systems?

The conversations we have been holding with clinicians and healthcare workers, legal experts, and health-policy researchers reveal a more nuanced picture than the narratives of efficiency or accuracy that dominate public discourse. AI in healthcare is not merely a new tool; it is a force that is reshaping how care is organised, how professionals are trained, and how patients experience the health system.

Here are some of the key insights emerging from our ongoing work:

1. AI is entering a system that is already overstretched, and this shapes every outcome

India’s health system suffers from longstanding structural gaps such as limited clinical training support, extreme caseloads, and a chronic mismatch between the number of healthcare workers and the scale of need. AI tools are being introduced into this environment with the hope and assumption that they will “fix” inefficiencies. But without strengthening the underlying system—institutional support, continuous training, regulatory coherence—AI risks becoming another layer of pressure on healthcare workers rather than a source of relief. In a context where doctors spend mere minutes with each patient, the stakes of misalignment can be high.

2. We cannot afford to skip “slow, deep science” and move directly to deployment

Across the sector, tools like AI-based chatbots for mental health, AI-based diagnostic devices, and so on, are being piloted rapidly and at scale, despite limited public evidence or rigorous clinical validation. In healthcare, where harm compounds quickly, scaling untested assumptions can institutionalise uncertainty. India needs transparent science pipelines, robust validation, and risk-tiered pathways before patient-facing AI becomes a normative practice.

3. The greatest promise of AI lies upstream

AI’s strongest contributions are emerging in areas that augment scientific capability rather than clinical judgement: drug discovery, epidemiological modelling, climate-health prediction, and pattern recognition in complex datasets. These domains minimise patient risk and maximise scientific gain. By contrast, patient-facing applications—triage systems, chatbots, automated summaries—raise questions of safety, accountability, data integrity, and explainability. Further, within clinical care, tools that augment clinical capabilities are considered safer than autonomous tools. This is because without strong oversight, autonomous tools risk shifting decision-making power away from healthcare workers and amplifying surveillance over care.

4. Accountability gaps could reshape the political economy of healthcare

Today, liability for AI-related patient/healthcare harms rests almost entirely with doctors, while the role of hospitals and AI vendors remains less clearly defined. As AI becomes embedded in diagnostics, workflows, and care decisions, this imbalance is poised to transform the healthcare landscape, potentially accelerating consolidation in corporate hospitals and undermining the viability of small clinics. Policymaking, certification processes, and medical-device regulation must urgently adapt to govern algorithmic risk, dataset bias, and model drift across diverse Indian populations.

5. Transparency and continuous monitoring are non-negotiable

Doctors who are integrating AI responsibly are already conducting local pilots, testing models against their own patient datasets, and a few are conducting continuous monitoring and evaluation to ensure that the tool performs as intended. But responsible adoption cannot depend on individual diligence alone. India needs transparent documentation of model development, independent audit capacity, clear disclosures on data use, and systematic monitoring frameworks. Without these, AI becomes a black-box extension of an already unequal system.

AI will inevitably reshape healthcare. The question is whether it will amplify care or scale harm. Ensuring a just, care-centred future depends on designing for patient safety, strengthening clinical agency, and resisting the temptation to treat technological possibility as policy inevitability.

For deeper insights, take a look at our flagship projects:

• From Principles to Practice: Responsible AI and Healthcare in India: Through this project, we are developing a context-specific, practice-oriented risk assessment framework for AI in Indian healthcare. We are creating a structured approach that helps medical institutions and professionals navigate AI risks effectively.
• Whose risk is it anyway? Developing a framework for Responsible AI in Indian Healthcare: This blog by Harleen Kaur and Aayushi Vishnoi offers an introduction to our ongoing work above.
• Towards Responsible Data Practices for Machine Learning in India: Health & Agriculture: This study examines the data practices entailed in building AI systems for healthcare and agriculture in India as a way to enquire the foundations on which the promises of AI as an instrument of social and economic progress are based.
• Graphic Short: ‘Towards Responsible Data Practices for Machine Learning in India: Health & Agriculture’: Accompanying the report above, this graphic short highlights the challenges faced by different, integral workers in the data life-cycles specific to healthcare and agriculture in India.