Trusting clinical AI starts with understanding the language: a practical guide to the terms every clinical leader should know

This article argues that clinical AI trust is not earned by impressive algorithms, but by disciplined evidence and shared understanding. It begins with ground truth, the reference standard used to judge whether a model is correct. Every performance metric depends on it. If the reference is inconsistent, strong-looking results can still mislead leaders. Because of this, validation studies alone are not enough. Regulatory clearance shows a model works somewhere, but clinical validation shows it works in a specific hospital, with its own workflows, scanners and patient population. Real deployments often reveal performance changes that controlled studies never exposed, including measurable drops in precision after go-live. Continuous monitoring, not one-time testing, is what sustains trust.

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