In the five years since the pandemic, decentralized trial elements have solidified their status in medical product development. 

Trials with decentralized elements have moved past the “pilot” phase. The question is no longer whether we can operationalize decentralized trial components, it's whether we’re doing it thoughtfully at the pace patients deserve. Our industry is ready to optimize the elements for the trial based on available evidence.  

That’s exactly why Medable, in collaboration with and facilitated by the Tufts Center for the Study of Drug Development (Tufts CSDD), has launched the Innovation Evidence Workshop series. 

Last November, the inaugural, invitation-only workshop brought leaders from 20 pharmaceutical, biotech, and CRO organizations together in Boston, with representation from the U.S. Food and Drug Administration, Harvard MRCT Center, Tufts CSDD, and Medable.

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Artificial intelligence continues to influence nearly every industry, and life sciences are no exception. In a recent conversation on the AI and Business podcast, Damien Nero, Head of Data Science in US Medical at Takeda Pharmaceuticals, shared his perspective on how AI is changing the clinical trial landscape. With over 15 years of experience applying machine learning and real-world data to drug development, Nero outlined both the progress already being made and the challenges that still stand in the way of broader transformation. His insights highlight how pharmaceutical leaders can think strategically about deploying AI to balance innovation with operational efficiency.

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