AI has significant potential to improve clinical trial planning.
It can help analyse large datasets, compare competing studies, model protocol scenarios, identify patient subgroups and support decisions about endpoints or site selection. Used well, it may allow sponsors to challenge assumptions earlier and avoid some preventable design errors.
But an AI-optimised protocol is not automatically a recruitable protocol.
A study can be scientifically rigorous, statistically efficient and operationally modelled in detail, yet remain unattractive or impractical for the patients and sites expected to deliver it.
This is because optimisation depends upon the objective being pursued.
A narrower patient subgroup may improve scientific precision while drastically reducing the available population. Additional assessments may improve data quality while increasing travel, time and procedural burden. More frequent monitoring may appear prudent in a protocol model while making participation unrealistic for people who work, care for relatives or live far from a research site.
Similarly, a site may appear highly experienced in historical data while currently lacking coordinator capacity or facing intense competition from overlapping studies.
Patients do not experience a trial as an elegant protocol. They experience appointments, travel, uncertainty, procedures, possible side effects and disruption to everyday life.
Sites do not experience it as a strategic asset. They experience screening workload, staff availability, competing priorities, training requirements and the difficulty of finding people who genuinely match the eligibility criteria.
Those realities need to be considered before recruitment begins.
AI can improve the evidence available to decision-makers, but it cannot guarantee that the right questions are being asked. If a trial is optimised primarily for statistical power, scientific interest or regulatory confidence, the resulting design may still perform poorly when confronted with real-world participation.
This has direct relevance to AI Visibility as well. Publicly available information may reveal that a study is difficult to explain, appears unusually burdensome or compares poorly with competing options. An audit can expose those signals, but improved wording alone cannot repair a fundamentally unattractive design.
The most valuable use of AI is therefore not to replace patient, site or recruitment expertise. It is to help those perspectives enter the design process earlier and with better evidence.
The key question is not simply:
Can AI help us design a better trial?
It is:
Better for whom—and against which definition of success?
A trial that cannot recruit or retain participants cannot be considered optimised in any meaningful operational sense.
Read the full Clinical Leader article – originally published on 25 June 2026