The second part of Clinical Tech Leader’s examination of AI and clinical trial recruitment moved beyond what conversational access to ClinicalTrials.gov can technically do and considered what it changes in practice.
The distinction is important.
AI can make information retrieval dramatically faster. Teams can explore competitive trial density, review common endpoints, identify active investigators and compare registered studies through conversational queries rather than laborious manual searches.
This can improve planning and support more informed scrutiny of assumptions made by sponsors, CROs and feasibility teams.
For example, an AI-assisted analysis may show that several studies are competing for a similar population within the same geography or using the same investigators. It may reveal that a projected recruitment rate is based on comparators that are no longer genuinely comparable.
That is valuable intelligence.
The danger arises when better access to information is mistaken for recruitment readiness.
A comprehensive competitive analysis does not remove restrictive eligibility criteria. It does not reduce patient travel, simplify a difficult visit schedule or give an overstretched coordinator more capacity. It cannot determine with certainty which investigator will continue to prioritise the study when several sponsors are demanding attention.
The interface has improved, but the underlying operational reality remains.
This is why I argued in the feature that AI may improve planning more quickly than it improves delivery. Sponsors can benchmark more intelligently and challenge forecasts earlier. Yet unless the industry also changes how trials are designed, how sites are supported and how participant burden is managed, the overall recruitment outcome may remain disappointing.
This principle is central to my wider approach to AI in clinical trials.
AI should be used to expose assumptions, reveal inconsistencies and accelerate useful analysis. It should not be treated as an oracle or a substitute for experienced judgement.
Clinical Trials AI Visibility Audits follow the same discipline. They examine how AI tools interpret and compare publicly available information, but the findings are considered through the realities of patient recruitment, site selection, feasibility, trust and operational delivery.
A technically sophisticated answer can still be commercially or operationally misleading. A highly visible trial can still be difficult to recruit. A site that looks strong in historical data may still lack current capacity.
The objective is not greater use of AI for its own sake. It is better decisions.
AI can help clinical research teams see the landscape faster and from more angles. The responsibility for interpreting that landscape—and acting on it intelligently—remains human.
This commentary relates to the second part of a Clinical Tech Leader feature written by Chief Editor John Oncea, incorporating my analysis of AI’s practical value and limitations.
Read Part Two: AI in Clinical Trials—Real Impact, Real Limits, What’s Next