AI and Preventive Healthcare: Trust, Ethics and the Future of Care

As India shifts from illness-driven treatment to prevention-first healthcare, Even Healthcare is emerging as one of the few companies attempting to redesign the entire care experience end-to-end. The Bengaluru-based “payvider” — a model that combines healthcare delivery with insurance — offers members unlimited primary care, diagnostics, care coordination, chronic care management and cashless hospitalisation through its own clinical teams and partner network. In a conversation with CiOL, Alessandro Ialongo, Co-founder, Even Healthcare, explains how AI could accelerate this shift by improving patient engagement, strengthening clinical oversight, protecting data privacy and enabling a more equitable and trusted healthcare system, especially in underserved regions.

AI has already shown promise in radiology and oncology. Where do you see its greatest potential to fundamentally reshape preventive healthcare in India?

Preventive healthcare is fundamentally about engagement and trust-building. Encouraging patients to undergo additional tests or take proactive actions, especially when they have no symptoms, requires behavioural change and consistent motivation. That means multiple touchpoints and a relationship built over time. We believe AI can help sustain these interactions by following up with patients, sharing insights on health status, ensuring adherence to treatment, and helping doctors make patients feel cared for over months and years, not just during treatment. This continuity has a measurable impact on outcomes and plays a major role in fostering trust.

Trust remains the biggest barrier. How can we ensure patients and doctors have confidence in machine-led predictions without losing individual context?

We are not yet at the point, both from a legal and AI reliability standpoint, where AI systems can perform all aspects of a doctor's job in a completely unsupervised manner. At Even, we ensure that while many elements of the clinical workflow are automated with AI, the final decision or approval always comes from a human doctor. However, we also think that a clinical workflow that doesn’t leverage AI for quality control, automation, or a second opinion is one that is more exposed to human error, and that over the next few years we will see patients use AIs more and more for checking diagnoses and other clinical decisions. We believe building this level of quality control natively in our clinical practice is an important step in building trust with patients.

Data is both an enabler and a risk. What does true ethical stewardship of patient data look like in AI-driven preventive healthcare?

India’s new DPDP Act is a step forward in creating clear regulatory frameworks. Combined with explicit user consent for using anonymised patient data for AI training, Indian healthcare companies can responsibly balance privacy with innovation. At Even, we are fully HIPAA-compliant and only share data externally when it is absolutely necessary for providing care. Protecting patient confidentiality remains at the core of how we build and deploy our technology.

Even Healthcare is focused on keeping people healthy, not just treating illness. How can AI help realign incentives for insurers, providers, and patients around prevention?

Incentive alignment is a very challenging problem in healthcare. We are addressing it at Even by creating a single ecosystem for insurance and healthcare (the “payvider” model), which rewards us financially for better health outcomes: the healthier our members are, the lower our insurance costs. Outside this model hospitals only make money when patients are sick and are not incentivised to invest in prevention. Established and evidence-based clinical protocols can also be overridden in favour of practices that drive revenue up. To avoid this, we are developing and rolling out AI-driven quality control mechanisms that apply to every consultation and clinical recommendation. We believe strict clinical reviews are the only way to ensure healthcare can be administered fairly and in an evidence-based manner. On prevention specifically, it can bring down costs through better risk modelling, ensuring efficient resource allocation (e.g., determining who should be screened due to elevated risk), as well as reducing the cost of patient engagement, allowing insurers to play a more active role.

Rural-urban divides in healthcare are stark. How can digital platforms and AI bridge these gaps without reinforcing biases from limited datasets?

I think the benefits of democratising access to the latest healthcare practices — especially in rural settings where basic medical advice for ordinary pathologies is not immediately available — far outweigh the potential pitfalls of biased datasets. While adjustments and fine-tuning will certainly need to be made, and as mentioned, human oversight will be important at first, we think India has a unique opportunity to lead the way in applying AI-assisted healthcare widely, including in rural settings where the potential upside is greater. Ultimately, without controlled pilots, we would not be able to iterate on and improve the way AI can help us deliver better healthcare, more widely.

Looking ahead, what’s the single most important reform — technological, regulatory, or cultural — that would make AI truly trustworthy and inclusive in healthcare?

Cultural and regulatory shifts will naturally follow once the underlying technology becomes more transparent and reliable. We are already seeing a shift in perception driven by the accuracy and generalisation capabilities of the latest generative AI models, similar to what we have witnessed with autonomous driving becoming more accepted and trusted. The next significant steps for healthcare AI are eliminating hallucinations, quantifying uncertainty in model outputs, and making a model’s reasoning and assumptions visible and verifiable. Once these safeguards are in place, we will see faster adoption, growing clinician and patient confidence, and regulatory frameworks that evolve to support responsible AI integration in healthcare.