Accountable Intelligence: Why India must get healthcare AI right

By Deepak Lamba, Founder & CEO, ImagiNxt

AI has already changed how we work, shop, consume content and manage money. In most sectors, when AI gets something wrong, the cost is usually inconvenience. A poor recommendation on a streaming platform means a forgettable evening. A flawed digital process may delay a transaction or frustrate a customer. These are real problems, but they are rarely irreversible.

Healthcare is different. Here, a recommendation can influence a diagnosis, shape a treatment decision or affect a patient’s outcome. The stakes are not measured in inconvenience; they are measured in lives.

That is why AI in healthcare must be held to a higher standard than almost any other sector. The promise is significant, and India has a genuine opportunity to use it well. The question is no longer whether AI should enter healthcare. That debate is settled. The question is how we ensure it enters with evidence and accountability.

India’s healthcare system is under real pressure. A large population, uneven distribution of specialists, rising chronic disease burden and persistent urban-rural access gaps are not new challenges. What has changed is the urgency with which they demand solutions. The system needs better tools, faster decision-making and stronger access models. This is where AI makes a compelling case.

Consider what is already possible. A retinal image taken at a primary health centre can help identify early signs of diabetic retinopathy. A chest X-ray from a district hospital can be supported by an AI layer that flags possible abnormalities for faster clinical review. A frontline health worker can use a digital tool to identify high-risk patients who need follow-up. These are practical applications that address some of India’s most pressing healthcare gaps.

The digital foundation is also strengthening. Under the Ayushman Bharat Digital Mission, over 100 crore health records have been linked with ABHA, and more than 450 health technology solutions have integrated with the national health data framework. India’s AI in medical diagnostics segment is projected to grow from USD 69.8 million in 2025 to USD 584.8 million by 2034, signalling opportunity and institutional confidence. In healthcare, however, market size cannot be the real measure of success. The true test must be clinical value and better outcomes.

One of AI’s most important contributions could be shifting India’s healthcare system from reactive to preventive. Today, many patients reach hospitals only after an illness has advanced significantly. AI can help change that pattern by identifying risks earlier, supporting screening at scale and helping doctors make faster, better-informed decisions. In outpatient care, chronic disease management and maternal and child health, it can help direct limited clinical resources where they are needed most.

Encouragingly, some of this is already underway. AI-enabled tools have been deployed within national health programmes, including tuberculosis elimination, diabetic retinopathy screening and disease surveillance. In the private sector, Apollo Hospitals has publicly launched AI-led tools for cardiovascular disease risk prediction and AI-enabled early-warning patient monitoring. What these examples share is important: AI is being embedded within real care workflows, not positioned as a replacement for clinicians.

Another useful sign is that the conversation is beginning to move beyond individual hospitals, health-tech pilots and policy announcements. It is becoming a wider ecosystem question about how India builds healthcare AI that can be trusted in real care settings.

AI works best when it operates within existing care systems, under clinical oversight and with institutional ownership. Its greatest value is not in replacing healthcare; it is in strengthening it. Patients share sensitive information and make consequential decisions based on what they are told. In healthcare AI, consent, data protection, interoperability, auditability and explainability cannot be treated as compliance checkboxes; they are core clinical infrastructure.

This means the risks cannot be minimised or deferred. A model may influence which patients are prioritised, which cases are escalated and what a doctor reviews first. When those decisions are wrong, the consequences are clinical. Responsibility must therefore be built into the design from the start. AI tools must be tested across diverse settings, monitored after deployment and clearly owned by institutions that understand both technology and care delivery.

India’s diversity makes this especially critical. A model trained on narrow or skewed datasets may not perform equally across regions, languages, age groups, disease patterns or socio-economic conditions.

For AI to work meaningfully in India, it must be trained, validated and audited in Indian conditions, not simply imported and assumed to translate.

There is also a broader mindset shift needed. The fear that AI will replace doctors persists in many conversations, but it misrepresents what good AI does in a clinical setting. The more accurate picture is a support layer that reduces administrative burden, flags patterns a busy clinician might miss, assists with triage and improves information at the point of care. A radiologist reviews more cases with sharper prioritisation. A physician accesses patient history without wading through fragmented records. A nurse identifies which patient needs follow-up first. The future is not doctor versus AI. It is doctor plus AI, working together.

The health-tech companies that will endure are those that invest in clinical validation, partner seriously with healthcare institutions, handle patient data responsibly and build credibility through demonstrated outcomes. Trust will not come from branding or bold technology claims. Doctors need confidence in the tool. Patients need to understand how their data is being used. Regulators need visibility. Founders need to prove outcomes, not just pitch potential.