AI RESEARCH
Robust by Design: A Continuous Monitoring and Data Integration Framework for Medical AI
arXiv CS.CV
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ArXi:2604.09009v1 Announce Type: new 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.