Unlocking the Potential of Agentic AI to Reduce Trial Delays

In a recent video interview with Applied Clinical Trials, Michelle Longmire, MD, co-founder and CEO of Medable, discussed the role of agentic artificial intelligence (AI) in clinical trials. She highlighted that 45% of clinical development time is wasted due to delays and siloed data. Agentic AI aims to reduce this by leveraging technology in areas previously untouched, freeing up human resources for strategic work. The human-in-the-loop concept ensures that AI supports, rather than replaces, human decision-making. Integration of AI across legacy systems is facilitated by model context protocols, reducing manual effort. Automation handles routine tasks, while critical decisions remain human-controlled. No-code platforms for AI tools require clear job descriptions and validation similar to human training.

ACT: With clinical trials still facing major inefficiencies, what role do you think AI-powered agents could play in reducing delays across the trial lifecycle?

Longmire: The delays in clinical development are one of the major bottlenecks and barriers, and when you look at a clinical trial or the overall development life cycle, 45% of time is white space. That means it's time lost to delayed decision making, time lost to data being siloed, and 45% of the total timeline is obviously something very meaningful. The bigger issue I see is pre-clinical science is at an all-time high. What does that mean? We have many more assets and medicines that we want to test then the current time and cost would permit us to test. What excites me about agentic AI is if we remove that white space and we're able to reduce the time and reduce the cost, we'll be able to take more shots on goal, which will mean more treatments and cures for disease. I think that agentic AI is such a powerful lever, simply because it's actually bringing technology to areas that have never benefited from technology before, and it really enables us to shift the human energy in the human workforce up the performance curve from redundant tactical work to highly strategic work, and offload that tactical work to agentic AI.