AI agents for clinical trial operations
Site selection, recruitment, and enrollment forecasting run on historical performance and real patient data rather than assumption. Monitoring runs continuously, so a finding worth catching on Monday is caught on Monday — with the same assembled context for every reviewer.
Lower lag, higher consistency
Sites ranked on evidence, not relationships
Site selection usually rests on who you worked with last time. TrialMind ranks sites on historical performance, investigator experience, and the patient population actually in their catchment — so the trial starts with sites that can deliver.
Enrollment and dropout you can forecast
Enrollment projections are usually assumption dressed as a plan. TrialMind predicts enrollment velocity per site and dropout across the study from real patient and historical trial data, and identifies the bottleneck before it becomes a timeline slip.
Data and medical monitoring
The problem is not detection — it is lag and consistency. A borderline subject takes 30 to 90 minutes of manual context gathering, weekly batch review means a signal worth catching on Monday is caught the following Monday, and two monitors reconstruct the same case differently.
Built so the monitor stays in charge
AI in clinical decision-making is where compliance says no, and rightly. The boundary here is architectural, not a policy promise.
The human keeps the clinical call
TrialMind adjudicates evidence. Your medical monitor adjudicates patient care. That line is built into the design rather than negotiated with compliance later.
Every rationale cited and auditable
Each finding arrives with the source records behind it, so a monitor can check the reasoning instead of trusting a score.
Co-designed with your monitors
We build around how your team actually reviews cases, rather than asking them to adopt someone else's review process.
Why not just use a general-purpose model?
A general-purpose model can flag an outlier. The work that actually costs your monitors time is the 30 to 90 minutes of context reconstruction behind each borderline case — and the reason two monitors reach different conclusions is that they reconstruct it differently.
We co-design this with your medical monitor around how your team actually reviews cases. If you want to start somewhere more proven, bring us a protocol and we will rank your sites.
Frequently Asked Questions
How mature is the medical monitoring capability?
Site selection, recruitment, and enrollment and dropout prediction are in active use. Continuous medical monitoring with a disagreement-aware review panel is newer — it has been formally proposed and co-designed with medical monitors rather than delivered at scale across many trials. We would rather tell you that than let you find out during the engagement.
Does this make clinical decisions?
No, and it is not designed to. The system adjudicates evidence — assembling and citing the context behind a finding. The medical monitor adjudicates patient care. Every rationale is cited and auditable so the human making the call can check the reasoning.
What happens when the analysis is uncertain?
Disagreement is surfaced to the monitor as a signal rather than averaged into a single confident score. A case where the evidence genuinely conflicts is exactly the case a human should be looking at.
How does this fit our RBM framework?
TrialMind can develop an RBM framework per ICH E6(R2) — critical data elements, risks, and KRIs with thresholds — or work inside the framework you already run. It produces site risk scores, targeted visit agendas, data quality checks, and query recommendations against it.
What data does site selection use?
Historical site and investigator performance, therapeutic-area experience, and the patient population in each site's catchment, evaluated against your protocol's eligibility criteria. The output is a ranked site list with the reasoning attached, not an opaque score.
Does our patient data leave our environment?
No. TrialMind deploys into your environment and runs where the data already lives, which is what makes security and privacy review passable for EHR-based pre-screening.
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