[2604.04878] Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices

Computer Science > Artificial Intelligence arXiv:2604.04878 (cs) [Submitted on 6 Apr 2026] Title:Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices Authors:Alexis Burgon, Berkman Sahiner, Nicholas A Petrick, Gene Pennello, Ravi K Samala View a PDF of the paper titled Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices, by Alexis Burgon and 4 other authors View PDF HTML (experimental) 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 performance changes caused by model adaptations versus dynamic environments. Case studies using simulated population shifts demonstrate the approach's utility: gradual transitions enable stable learning and retention, while rapid shifts reveal trade-offs between plasticity and stability. These measurements provide practical insights for regulatory science, enabling rigorous assessment of the safety and effectiveness of adaptive AI systems over sequential modifications. Subjects: Artificial Intelligence (cs.AI); Performance (cs.PF) Cite as: arXiv:26...