Every completed trial leaves behind a record: what design it used, who it enrolled, how many participants dropped out, how long enrollment actually took, and whether the design held up. Across thousands of past trials, these records form a large collection of design decisions and their real outcomes. AI's role here is pattern finding: scanning that collection for regularities that are impractical to spot by hand. The result is a design suggestion — for example, that trials with a certain eligibility range and visit schedule in this disease area have historically enrolled at a workable pace, while a narrower range has repeatedly stalled. The suggestion is a ranked guess drawn from precedent, not a guarantee, and it still needs human judgment to weigh against the specific science of the new trial. But it replaces a starting point built only on personal experience and a handful of remembered studies with one built on the accumulated record.
Learn MotionHow AI Makes Clinical Trials Faster: A High-Level Overview
Faster Study Design and Protocol Planning
Learning From Trials That Already Ran
2 / 4
Follow the flow from left to right. On the left are many completed trials, each leaving a record of its design and what actually happened — how many people enrolled, how many dropped out, how long it took. In the middle, those records are pooled and scanned for patterns. On the right, the output is a short list of design suggestions. The important thing to notice is the arrow: information moves from accumulated experience toward a recommendation, not the other way around. And notice how the recommendation is drawn — as a suggestion, not a verdict. It is a ranked guess from precedent that a human still has to weigh against the science of the new trial.
0:00 / 0:00