Statistical techniques for detecting when clinical input data diverges from training distributions.
Replacing an older 64-slice CT scanner with a modern dual-source system alters noise texture and spatial contrast. If an AI model is not calibrated to the new scanner, false positive lung nodule detections spike dramatically.
Two-sample Kolmogorov-Smirnov tests compare daily image feature distributions against baseline validation sets, alerting clinical directors before inaccurate predictions reach physician reports.