Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices
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 t...
| Authors | Alexis Burgon, Berkman Sahiner, Nicholas A Petrick et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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