Concern 736 · Working draft · editorial review in progress
AI in clinical trials
AI-related clinical-trial bias occurs when automated recruitment or monitoring overlooks some participants, misreads their records or measures outcomes unevenly.
Understanding the concern
A matching tool can compare a patient's record with trial criteria and suggest studies worth considering. But missing information may be mistaken for ineligibility, and people with less complete records may be missed. During a trial, monitoring tools can also work differently across devices or participant groups.
Trial-matching research shows promising assistance on defined tasks. It does not establish that an automated pipeline recruits fairly or that a suggested match confirms eligibility. Think of a search filter that helps find candidates but should not silently close the door. Human review, checks across groups and careful handling of health information remain necessary.
An example
A potentially eligible person is never shown to a recruitment team because a key fact is absent from their electronic record.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
Evidence and its limits
The source supports patient-to-trial matching, not all monitoring uses. The broader monitoring concern requires tool-specific validation and is not measured by this study.
Jin and colleagues · Nature Communications 2024 · Clinical trial matching research
Matching patients to clinical trials with large language models ↗ (opens in a new tab)TrialGPT on defined cohorts and matching tasks. Does not establish equitable recruitment or clinical monitoring outcomes in every setting.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
A question to discuss
Who might be missed by this matching process, and how will the team look for them?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Learning together
Learning together means treating experience as something a group can examine and improve. It values honest feedback over appearing successful.
Agree on a question, try something manageable, and compare what different people observed. Share failures as well as gains. Decide together what the evidence supports and what remains uncertain.
Ask together: What have we learned together that should guide the next step?
Humility
Humility means recognizing the limits of our knowledge and being willing to learn from others. Expertise can be valuable without answering every question.
State where evidence is weak, invite criticism, and listen to people with different experience. Leave room to revise a decision when new information appears.
Ask together: What might we be missing, and who could help us see it?
Truthfulness
Truthfulness means making claims that match the evidence and being clear about what we do not know. It includes correcting mistakes rather than protecting a polished story.
Check sources, label guesses, and distinguish a trial result from a proven benefit. If AI helped produce a claim, someone still needs to take responsibility for checking it.
Ask together: What can we honestly say, and what still needs checking?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
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