AEGIS: an operational infrastructure for post-market governance of adaptive medical AI under US and EU regulatory frameworks

Medical AI requires governance preserving safety during continuous improvement. FDA Predetermined Change Control Plans (PCCP) and EU post-market surveillance address iterative updates but lack operational tooling. We present AEGIS, an internal governance and post-market-monitoring infrastructure designed to support the documentation of predetermined governance pathways for PCCP and Medical Devices Regulation alignment. AEGIS comprises dataset assimilation and retraining, model monitoring, and conditional decision-making modules. It utilizes a four-category taxonomy (APPROVE, CONDITIONAL APPROVAL, CLINICAL REVIEW, REJECT) and an independent ALARM signal. Instantiating AEGIS for sepsis prediction (PhysioNet) and brain-tumor segmentation (BraTS) exercised all decision categories, successfully routing failures to the critical REJECT + ALARM state (no deployable model; released model at risk). On real cross-site data, drift detection ROC-AUC rose from 0.82 to 1.00, reliably triggering near 500-patient batches with zero false alarms. This proof-of-concept utilizes illustrative clinical models; drift scoring quantifies covariate shift only, leaving concept drift for future work.