Preventing catastrophic performance drops when deploying diagnostic models across multi-center hospital networks.
Changes in CT scanner manufacturer, laboratory assay reagents, or patient demographic profiles introduce covariate shift. If undetected, an algorithm that achieved 98% accuracy in development may drop below 70% in community clinics.
By evaluating feature embeddings using two-sample testing (Maximum Mean Discrepancy and Wasserstein distance), the platform detects silent drift before clinical misdiagnoses occur.