Engineering Trustworthy AI for Healthcare: From Medical Data to Clinical Intelligence
A few months back I was reviewing a sepsis prediction model for a client, and the model itself was fine. Decent AUC, reasonable calibration on paper. What stopped me was a throwaway comment from one of the ICU nurses during a workflow walkthrough: "we don't really look at that score anymore, it fires too often." That single sentence told me more about the state of trustworthy AI in healthcare than any benchmark table could. The model was technically accurate and practically useless, because nobody had engineered the layer between prediction and decision.
That gap, between what a model outputs and what a clinician can actually act on, is where most healthcare AI projects quietly fail. Not in accuracy. In trust. And trust, it turns out, is not a soft add-on you sprinkle at the end. It has to be architected in from the moment a patient's data is captured, all...
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