A model flags the right diagnosis. The risk score is accurate. The prediction holds up in validation. And the patient still gets the wrong care. This session moves past the accuracy question — the one most AI conversations stop at — to confront what happens after the algorithm is right: alerts that get lost in the noise, clinicians who don't trust a score enough to act on it, workflows that were never built to carry an AI recommendation the last mile to the bedside. Being correct is not the same as being useful. We'll look at real cases where good predictions failed to become good outcomes, and what it actually takes to close that gap — trust, workflow design, and accountability, not just model performance.