Smart Care with AI Turning Patient Centricity into Real Impact

Artificial intelligence is moving fast across life sciences, reshaping how trials are designed, therapies are developed, and patients are engaged. But beneath the momentum sits an uncomfortable question many organizations are only beginning to confront: is AI making healthcare feel more human, or more complicated?

That question anchored a thought-provoking session at the Indegene Digital Summit 2025 (Virtual Edition), where Trishna Bharadia , Patient Advocate and Patient Engagement Consultant at The Spark Global, challenged pharma leaders to rethink what “smart care” really means in an AI-driven world. The conversation deliberately moved beyond tools and algorithms to focus on something harder, and more consequential, how patient centricity in clinical trials and care models must evolve as AI becomes embedded across the value chain.

The timing could not be more relevant. Patients are already using AI to search for health information, interpret symptoms, and navigate care decisions. Expectations around speed, clarity, and personalization  have fundamentally shifted. For life sciences organizations, the mandate is no longer just to adopt AI, but to ensure innovation aligns with real patient needs, not internal efficiencies alone.

The most valuable role of AI in healthcare is not novelty. It is its ability to solve long-standing problems that have resisted traditional approaches for decades. Clinical trial optimization, faster discovery cycles, and smarter patient engagement are only meaningful if they lead to better outcomes for people living with disease.

Across research and development, AI is shortening timelines by identifying viable targets faster and improving trial readiness. In parallel, patient advocacy groups are increasingly using AI to improve trial awareness and recruitment , helping studies reach more diverse populations earlier.

What stands out is not the technology itself, but how it reframes priorities. When AI is applied with intent, it reduces friction for patients rather than adding new layers of complexity. That shift from operational efficiency to experiential impact is where real value begins.

Some of the most patient-relevant AI use cases remain under-discussed. One such area is clinical trial logistics . For patients, participation is often shaped less by protocol design and more by practical realities, travel burden, scheduling conflicts, medication access, and coordination across care teams.

AI-driven forecasting and logistics optimization are beginning to address these challenges. More accurate demand planning reduces shortages and delays. Better visibility into supply chains improves medicine availability and authenticity. For patients managing multiple therapies, this directly affects safety, adherence, and confidence in treatment.

These improvements rarely make headlines, yet they meaningfully shape patient experience. Digital patient engagement  does not start with apps or chatbots; it starts with ensuring that the basics of care work reliably.

AI has unlocked powerful opportunities for personalization across clinical trials and patient communications. Tailored reminders, adaptive content, and behavioral nudges can improve participation and adherence when designed thoughtfully.

However, personalization without validation introduces risk. Early evidence shows that while generative AI tools can produce highly consistent responses to patient queries, accuracy remains uneven. This creates both a challenge and an opportunity for pharma.

Organizations that invest in medically validated, AI-enabled content, particularly around treatment education and trial participation, can fill a growing trust gap. Plain language summaries, developed with scientific rigor and patient readability in mind, become essential in this context. They help patients understand complex information without oversimplification, reinforcing confidence rather than confusion.

The lesson is clear: AI should support decision-making, not replace clinical judgment or human oversight.

As digital patient engagement expands, trust has emerged as the limiting factor. Patients are open to AI-enabled experiences, but only when transparency and accountability are visible.

This is especially true in regulated environments such as clinical trials and pharmacovigilance. Patients want to know how their data is used, how AI-driven insights are generated, and where human review still plays a role.

Effective patient engagement AI strategies recognize this reality. They combine explainability, ethical design, and clear escalation paths to human support. Omnichannel engagement, social listening, and contextual nudges can enhance experience, but only when patients feel respected rather than monitored.

Trust is not built through technology alone. It is built through consistency, clarity, and responsiveness over time.

One of the most critical shifts discussed was the need to involve patients earlier and more meaningfully in AI initiatives . Too often, patient input is sought late, framed as validation rather than collaboration.

Embedding patient perspectives across the AI lifecycle, from problem definition to model evaluation, changes outcomes. Different contributors bring different value. Experienced patient advocates, condition-specific experts, and patients-by-experience each offer distinct insights that technology teams cannot replicate.

When patients feel genuinely heard, engagement improves. More importantly, AI solutions become better aligned with lived realities rather than assumed needs. This approach transforms patient centricity in clinical trials from a principle into a practice.

The most grounded insight from the session was also the simplest.

Used responsibly, AI can strengthen patient engagement, streamline clinical trial logistics, and improve access to reliable information. Used poorly, it risks distancing organizations from the very people they aim to serve.

The path forward requires balance. Human judgment must remain central. AI should amplify empathy, not erode it. When technology supports care rather than defining it, smart care becomes more than a strategy, it becomes a shared commitment between pharma and patients.