Safety signals
Adverse-event patterns and emerging risks detected across sites and visits — surfaced early, ranked by relevance.
See how Leggy Labs helps clinical-trial teams identify and prioritise safety, protocol and data-quality signals — while qualified humans remain in control.
One flagged signal, from detection to human decision — on synthetic trial data.
Leggy Labs continuously reads your trial data and separates noise from what matters — then hands the prioritised shortlist to the people who decide.
Adverse-event patterns and emerging risks detected across sites and visits — surfaced early, ranked by relevance.
Deviations and eligibility or visit-window breaches identified against your protocol logic.
Missing, inconsistent or outlier data caught before it distorts the picture at lock.
Every detected signal lands in a prioritised review queue — scored by severity and confidence, tagged by family, and assigned to the right reviewer. Nothing is auto-actioned. The queue below is illustrative, built on synthetic data.
The model narrows the field. A qualified human makes the call. Every time.
“What changed for us wasn't automation — it was getting the right five signals to the top of the queue instead of drowning in a thousand. My monitors spend their time deciding, not searching.”
“The human-in-the-loop framing is why our governance team took it seriously. Nothing is auto-actioned — the platform surfaces, our reviewers decide.”
Tell us about your organisation and clinical-trial requirements. Our team will contact you to arrange a demonstration.
Leggy Labs is shaped around the workflows of the people who run trials — the reviewers, monitors and data leads who own every decision the platform supports.
Leggy Labs is currently demonstrated using synthetic clinical-trial data. The platform is not a medical diagnostic or treatment system. Clinical deployment requires appropriate validation, security, governance and regulatory assessment.