To adapt ML models to concept drift under strict resource constraints, we propose a lightweight drift-plus-penalty policy that provably limits resource usage and achieves robust results.

This paper argues that post-deployment monitoring in clinical AI is underdeveloped and proposes statistically valid and label-efficient testing frameworks to ensure reliability and safety.