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

arXiv:2604.04878v1 Announce Type: new Abstract: This work addresses challenges in evaluating adaptive artificial intelligence (AI) models for medical devices, where iterative updates to both models and evaluation datasets complicate performance assessment. We introduce a novel approach with three complementary measurements: learning (model improvement on current data), potential (dataset-driven performance shifts), and retention (knowledge preservation across modification steps), to disentangle