Clinical operations is quietly one of the better places for this technology, because so much of a trial is pattern-matching across large volumes of text and data — work that is slow, repetitive, and performed by people whose judgement is needed elsewhere.
It is also an area where the line between help and harm is unusually clear, because GCP is explicit about who decides what.
Where it works in a running study
Site selection and feasibility. Predicting which sites will actually enrol, from historical performance and local population data, instead of relying on relationships and optimistic estimates. Unglamorous and directly tied to timelines.
Pre-screening against eligibility criteria. Reading health records to surface patients who might qualify. The system proposes; a clinician confirms. This is the highest-value use in most trials, because recruitment is the most common cause of delay.
Risk-based monitoring. Finding the sites and patterns that deserve a monitoring visit — unusual data distributions, timing anomalies, outlier rates. Well matched to ICH E6(R3), which already expects a risk-based approach.
Data cleaning and query generation. Spotting inconsistencies that would otherwise surface at database lock.
Safety triage. Grouping and prioritising adverse event reports so that reviewers see the important ones first. Prioritisation, not assessment.
Document drafting. Protocol sections, narratives and report components drafted from study data for a medical writer to finish — see AI in clinical writing.
What stays a human decision
- Eligibility. A model can shortlist; an investigator decides whether a patient enters a trial. That is a medical judgement with a named accountable person.
- Causality assessment. Whether an adverse event relates to the investigational product is a clinical determination, and it carries regulatory consequences.
- Protocol deviations and their significance.
- Informed consent. A conversation with a person, not a workflow step.
- Unblinding and safety decisions.
The common thread is the same one that runs through all regulated AI: the system may propose, a qualified person decides, and the decision is recorded with their name on it.
The sponsor's problem
Most of this runs at the CRO or the site rather than at the sponsor — and under ICH E6(R3) the sponsor keeps oversight responsibility for work it does not perform. So the questions that matter are contractual and practical rather than technical:
- Which of your vendor's processes involve these tools, and where in the study?
- If a tool pre-screens patients, what happens to those it misses? A recruitment aid that systematically overlooks a subgroup becomes a representativeness problem in your data.
- Can the vendor show how a given output was produced, if a regulator asks?
- What happens to patient data — where is it processed, and is that consistent with your consent forms and your data protection obligations? See data privacy.
The bias question deserves more weight than it usually gets. A model trained on historical trial data learns historical enrolment patterns, including the ones the industry has spent a decade trying to correct. A tool that quietly reproduces them is a scientific problem before it is a compliance one.
Starting without creating a problem
Begin where an error is visible and recoverable. Site feasibility and data query generation are good first steps: wrong output is obvious, and nobody is harmed while you learn the tool's failure modes. Pre-screening is higher value and needs the clinician-confirmation step built in from day one, not added later.
Keep records of what the tool proposed against what the human decided. That comparison is both your evidence of real oversight and your early warning that reviewers have started rubber-stamping.
The method is in GxP AI validation, and the pilot structure in running a 90-day pilot. On the US terminology change, nothing above is affected — see what the rename means for life sciences.
Where to go next
Explore GxP Copilot for AI-native validation, TraceDraft for source-traceable clinical documentation, or book a demo to see either on your own data.