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How AI Makes Clinical Trials Faster: A High-Level Overview

1Why Clinical Trials Are Slow, and Where AI Fits In2Faster Study Design and Protocol Planning3Finding and Enrolling Participants Sooner4Keeping Participants in the Trial5Monitoring Data and Catching Problems Early6Analyzing Results and Reporting Sooner7What the Time Savings Add Up To, and What AI Cannot Fix
Finding and Enrolling Participants Sooner

Why Recruitment Sets the Pace of the Whole Trial

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Think about why the search itself is slow. Eligibility criteria are rules, and every rule you add removes people from the pool. The rules combine, so someone who satisfies all of them at once is rare. A trial has to look through a large population to find a small number of matches, and that looking is done by people reading records one by one. That is the bottleneck. And the delay does not stay contained: the trial cannot analyze its results until the last participant has been followed for the required period, so a slow recruitment phase pushes back every stage that follows it.
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Recruitment is usually the longest phase of a trial and the one most likely to overrun. Because the trial cannot finish until enough participants have been enrolled and followed, a slow recruitment phase pushes back everything after it.

Why the search is inherently slow

Eligibility criteria are the rules that decide who may join. Each rule removes people from the pool, and the rules combine, so the number of people who satisfy all of them at once is much smaller than the number who satisfy any one of them. A trial therefore has to look through a large population to find a small set of matches, and that looking is done by people reading records one at a time.

The cost is not linear. A trial that enrolls at half the planned rate does not simply finish a little late; it delays the point at which the last participant completes follow-up, which is when analysis can start. Recruitment delay is inherited by every later stage.

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