Clinical AI has an asymmetric safety profile: a false negative can delay treatment, a false positive can trigger an unnecessary intervention. Calibrated uncertainty and abstention — knowing when not to answer — are safety features, not edge cases.
A common gap: a clinical model outputs a confident-sounding result on every input, with no mechanism to flag when its own confidence is too low to act on.
Reviewing the trust-scoring architecture, evaluating abstention behavior against your clinical safety envelope, and identifying where confidence isn't calibrated to actual reliability.
Related real work:
MSc dissertation on trust-scoring for clinical AI: when a medical model's output should be believed, and when it should abstain
NHS GP scribe, a 119M-parameter Conformer ASR model trained from scratch for UK clinical audio
No. This is technical verification of the model and pipeline. Regulatory medical device certification is a separate, formal process.
It focuses specifically on trust-scoring and abstention — whether the system knows when it doesn't know — which general ML reviews often don't cover.
Access to your technical documentation and a diagnostic call about what needs verifying and why it matters to your specific system.