AI and real-world evidence: The next frontier in clinical research

As AI meets real-world evidence, clinical research is moving beyond controlled trials into the realm of real-life data. The result is a smarter, faster, and more equitable path to better healthcare

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In the rapidly evolving world of clinical research, few developments hold as much promise—or provoke as much debate—as the convergence of artificial intelligence (AI) and real-world evidence (RWE). While AI has already transformed industries from finance to logistics, its ability to analyse, interpret, and act on vast datasets in real time is now redefining how we design, conduct, and scale clinical research. For India, a country with immense patient diversity, digital health penetration, and growing R&D infrastructure, this convergence presents a transformative opportunity.

The shift from hypothetical to real-world

Traditionally, randomised controlled trials (RCTs) have been the gold standard in establishing drug efficacy and safety. However, they are time-consuming, expensive, and often limited in population diversity. Real-world evidence—derived from sources like electronic health records (EHRs), insurance claims, wearable devices, and mobile health platforms—offers a complementary approach. When powered by AI, RWE can reveal patient behaviours, treatment outcomes, and adverse events in ways that RCTs simply cannot.

India is uniquely positioned to lead this shift. With over one billion mobile phone users and increasing adoption of digital health tools, there is a growing trove of real-world data waiting to be structured and analysed. AI models can mine this data to detect patterns, predict outcomes, and optimise interventions with speed and precision.

In their recent article in ISPOR - Parexel, one of the world’s largest clinical research organisations (CROs) has clearly outlined some key areas where one could use AI in clinical development as follows:

Clinical trials reimagined