The least glamorous part of AI in radiology may turn out to be the most important: telling people what changed.
That sounds simple. It is not. A diagnostic AI tool may be trained on one dataset, validated on another, deployed inside a PACS or reporting workflow, monitored after release, and then updated when the model, threshold, input, interface, or intended environment changes. Somewhere in that chain, a radiologist is expected to decide whether to trust a box, a score, a contour, a triage flag, or a sentence.
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