Leveraging Artificial Intelligence Alongside RWE/RWD

In a recent video interview with Applied Clinical Trials, Sujay Jadhav, CEO of Verana Health, discussed potential advancements the clinical research industry could see in 2025. Jadhav highlighted how unstructured data like physician notes and imaging data can be leveraged to improve disease identification and progression tracking. He also talked synthetic control arms and how they can replace expensive non-treatment arms, enhancing trial efficiency.

Jadhav: Looking at how we leverage AI and real-world evidence/real-world data, it's going to play a substantial role, and it is starting to play a substantial role currently, right now, in unlocking the potential. There are multiple areas in which it is starting to unlock the potential. One that we're seeing and we're focusing on as well is predicting disease progression and assisting with early detection to support patient recruitment and retention, and so identifying suitable participants and helping reduce dropouts. The earlier you can do that, the more cost effective it can be in the overall clinical trial process. The other area where we're seeing and we're leveraging is identifying trends to inform data driven decisions across the product development life cycle, so helping improve the upfront process around how you optimize clinical trial design, and enabling adaptive trial design. Thematically, the more you can actually simulate what can happen with the particular clinical trial, and do that more upfront. That allows you to set up the actual trial from a logistical perspective more effectively and reduce costs and time as well. Also, as you go through that particular clinical trial leveraging AI to help support the adaptive trial design, if you need to actually change inclusion/exclusion criteria as you start to get results, leveraging AI to do that can be very, very helpful, as I said, and can help improve the overall effectiveness of the of the trial. The third area where we're seeing and leveraging AI quite a bit is enabling precision medicine, and that's by identifying subpopulations that are most likely to benefit from the treatment being investigated in a clinical trial, and ultimately helping drive and improve patient outcomes.