Transitioning medical AI from laboratory validation to live clinical hospital workflows carries immense patient safety risks. Shadow deployment runs candidate models in parallel with existing clinical workflows, processing real patient data silently without influencing treatment decisions. This paper presents technical metrics from an 18-month shadow deployment across three academic medical centers.
Kafka message brokers duplicate incoming radiology imaging streams, routing one copy to production diagnostic viewers and an identical copy to the shadow AI inference cluster with zero performance penalty on clinical PACS.
Tracking Cohen's kappa and F1 scores across 50,000 live imaging studies verified that the candidate algorithm maintained diagnostic equivalence across changing demographic seasons before receiving clinical deployment approval.