Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices
Alexis Burgon, Berkman Sahiner, Nicholas A Petrick, Gene Pennello, Ravi K Samala
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Why It Matters
This work introduces a standardized framework to evaluate AI medical devices that learn and adapt over time, solving a major regulatory bottleneck. It provides clear metrics to distinguish between a model actually improving versus just memorizing new data, which is critical for getting adaptive AI approved for clinical use.
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.