Clinical readiness

Whether a tool is ready to be used on patients, which is a different question from whether it performs well on a benchmark. Clinical readiness covers the things that decide whether a model survives contact with a real service: validation on the population it will actually see, calibration at the operating point you will run it at, a named owner for monitoring after go-live, a defined role in the workflow, and a plan for what happens when it is wrong. A model can post a state-of-the-art AUROC and meet none of them. Most published models never get this far, which is most of the reason the distance between a paper and a deployed tool stays so wide. For anyone evaluating a purchase, this list decides more than the headline metric does.

A department buys a triage tool that reported an AUROC of 0.94. Six weeks in, nobody can say who is meant to act on a flag, the alerts land in a queue two people watch inconsistently, and no one has looked at the score distribution since go-live. The model was fine. Everything around it was missing. Ask for the deployment plan in the same detail as the performance numbers, because that half decides whether the tool does anything at all.

Terms like this come up in real clinical scenarios across the HelloAI courses: bite-sized modules with verifiable certificates. An account takes one minute, no password needed.