We propose DIsoN, a decentralized method for out-of-distribution detection that enables a deployed model to directly compare incoming samples to the training data without data sharing.

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.