Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI
2026-04-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a method to keep medical AI models working well over time, even when new kinds of data appear. They monitor the AI’s confidence and similarity of new images to old ones before deciding to update the model. Their method carefully adds new data only if it’s similar and the model is confident, then retrains without losing accuracy. Tested on kidney disease images from multiple hospitals, this approach stopped the model’s performance from dropping. This helps medical AI continue learning safely without forgetting past knowledge.
data driftglomerular pathologylupus nephritisMonte Carlo dropoutuncertainty gatingfeature analysisResNet18AUCcatastrophic forgettingincremental retraining
Authors
Mohammad Daouk, Jan Ulrich Becker, Neeraja Kambham, Anthony Chang, Chandra Mohan, Hien Van Nguyen
Abstract
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.