Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices

Adaptive medical AI models often face performance drops in dynamic clinical environments due to data drift. We propose an autonomous continuous monitoring and data integration framework that maintains robust performance over time. Focusing on glomerular pathology image classification (proliferative vs. non-proliferative lupus nephritis), our three-stage method uses multi-metric feature analysis and Monte Carlo dropout-based uncertainty gating to decide when to retrain on new data. Only images statistically similar to the training distribution (via Euclidean, cosine, Mahalanobis metrics) and with low predictive entropy are integrated. The model is then incrementally retrained with these images under strict performance safeguards (no metric degradation >5%). In experiments with a ResNet18 ensemble on a multi-center dataset, the framework prevents performance degradation: new images were added without significant change in AUC (~0.92) or accuracy (~89%). This approach addresses data shift and avoids catastrophic forgetting, enabling sustained learning in medical imaging AI.

Machine learning systems deployed in medical devices require governance frameworks that ensure safety while enabling continuous improvement. Regulatory bodies including the FDA and European Union have introduced mechanisms such as the Predetermined Change Control Plan (PCCP) and Post-Market Surveillance (PMS) to manage iterative model updates without repeated submissions. This paper presents AI/ML Evaluation and Governance Infrastructure for Safety (AEGIS), a governance framework applicable to any healthcare AI system. AEGIS comprises three modules, i.e., dataset assimilation and retraining, model monitoring, and conditional decision, that operationalize FDA PCCP and EU AI Act Article 43(4) provisions. We implement a four-category deployment decision taxonomy (APPROVE, CONDITIONAL APPROVAL, CLINICAL REVIEW, REJECT) with an independent PMS ALARM signal, enabling detection of the critical state in which no deployable model exists while the released model is simultaneously at risk. To illustrate how AEGIS can be instantiated across heterogeneous clinical contexts, we provide two examples: sepsis prediction from electronic health records and brain tumor segmentation from medical imaging. Both cases use identical governance architecture, differing only in configuration. Across 11 simulated iterations on the sepsis example, AEGIS yielded 8 APPROVE, 1 CONDITIONAL APPROVAL, 1 CLINICAL REVIEW, and 1 REJECT decision, exercising all four categories. ALARM signals were co-issued at iterations 8 and 10, including the critical state where no deployable model exists and the released model is simultaneously failing. AEGIS detected drift before observable performance degradation. These results demonstrate that AEGIS translates regulatory change-control concepts into executable governance procedures, supporting safe continuous learning for adaptive medical AI across diverse clinical applications.

Clinical prediction models in intensive care degrade as data shift, and regulators such as the FDA ask developers to specify bounded update protocols in advance. Evaluations of such protocols assume that apparent drift reflects clinical practice rather than changes in how the database records care. We test whether update strategies can be evaluated on unaudited ICU data, and whether a parameter-bounded update gives up accuracy or attribution stability relative to full fine-tuning once the data are audited. We audit MIMIC-IV 3.1 (2008-2022) for recording artifacts and define a cleaning protocol. On the cleaned cohort, a label-free classifier two-sample test gates updates across three eras. We compare a bounded update, which retrains only treatment-context and fusion parameters, with full fine-tuning for two model families and two targets under a pre-specified non-inferiority margin of 0.015 AUROC, and measure attribution stability against the source model. In 2020-2022 about half of ICU stays have no input or procedure-event records, and a vasopressor ItemID migration makes the 2017-2019 label rate appear to fall when it actually rises. On the cleaned cohort, the gate detects drift in every era, including in physiology-only features, with no false alarms on null data. Bounded updating is non-inferior or superior in 10 of 12 primary cells, but this depends on the target, the model family and learning-rate tuning, and adaptation gains over never updating are small. At a shared learning rate, bounded updates keep physiological attributions more stable in neural models, clearly for vasopressor and less clearly for intubation; with separately tuned rates, full updates are more stable. Audits should precede update evaluation, and the update boundary should be chosen per target.