Veridical AI tests clinical and mental-health AI systems against clinically-authored safety scenarios and produces a reproducible, auditable safety attestation — the Label.
Clinical and mental-health AI is being deployed faster than it is being safety-evaluated.
A response can be fluent, warm and convincing — and still clinically dangerous: false reassurance that delays care, an asserted diagnosis, a dose that belongs to a prescriber.
Generic AI evaluation measures answer quality. Clinical safety asks a narrower, harder question: could this output harm a patient or a person in crisis?
Test cases encoding specific, literature-grounded patient-safety concerns — crisis escalation, diagnostic overreach, scope boundaries, medication safety — multi-turn where the risk demands it, each carrying its clinical rationale and citations.
Six clinical safety dimensions; safety-critical failures are non-averageable. A system that mishandles disclosed suicidal ideation is not “95% safe” — it is not attested, regardless of every other score.
Every run records the exact versions of library, scoring engine, judge and system under test, and produces a machine-readable attestation object plus a one-page human-readable Label. A safety claim that cannot be reproduced is not an attestation.
The Label is the human-readable face of a machine-readable attestation object. It states what was tested, at which versions, and whether the system passed the safety gates.
This example is deliberately a failing one — a real assurance artifact must be able to say not attested.
The evaluation engine, runner, report schema and example scenarios are open source under Apache-2.0 — seeding a common disclosure standard for clinical AI.
View the repository ↗The full curated clinical scenario library, calibrated scoring configurations, and a hosted attestation service producing signed, reproducible reports — delivered with high-touch clinical advisory.
Clinical-AI developers and digital-health companies.
Pharmaceutical and biopharma organisations deploying or procuring clinical AI.
Health systems and assurance bodies, as the standard matures.
Built by Dr J S Gill — a medical doctor experienced in clinical AI safety, previously at McKinsey & Company and Microsoft AI.
Veridical AI translates psychiatric clinical judgement into evaluation standards engineering teams can apply.