AIAC-05 · Sector track
For clinical safety, quality, and governance professionals assuring AI used in triage, diagnosis support, and healthcare operations, where error reaches a patient.
§ 1 — Intended candidates
Clinical safety officers, quality and governance leads, health-tech product and regulatory staff, and healthcare risk functions.
§ 2 — Examination domains
Domain weightings are published with the full syllabus ahead of the first cohort.
Pinning down what a system claims to do clinically, for which population, and where that claim stops.
Why headline performance on a retrospective dataset is not evidence of clinical safety in deployment.
Testing whether clinician review is a real control or an automation-bias formality.
Monitoring evidence needed to show a deployed model still performs on the patients actually presenting.
Investigating an AI-contributed clinical incident and feeding it back into governance.
§ 3 — How to certify
Preparation is optional. Choose whichever route suits how you already work — the examination, the fee, and the marking standard are the same either way.
Register and sit the examination. Suitable for practitioners already working in assurance who want the credential without preparation.
The same examination, bundled with self-paced preparation mapped to the syllabus. Taking it confers no advantage in marking and is never required.
Preparation is optional. The examination is available at the same fee and marked to the same standard whether or not preparation was purchased, and assessors are not told which route a candidate took. How we manage this conflict.
§ 4 — Assessment
Scenario-based examination set in a clinical deployment context supplied at the start, requiring a risk profile and an assessment of whether clinician oversight is a real control.
Successful candidates receive a credential with a unique identifier that any employer can check against the public record. See credential verification.
Curriculum mapping indicates alignment of learning content with published frameworks. It does not constitute endorsement by, or certification under, any standards organization or regulator.
§ 5 — Further reading
Producing credible, independently reviewable evidence that an AI system works as intended — and how that differs from governance and compliance.
A seven-step method for producing a risk profile a sceptical reader can challenge, with the failure mode at each step and what the finished artefact contains.