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

2026-04-06

AI Summary

This study addresses the challenge of attributing performance changes in adaptive artificial intelligence (AI) medical devices during continuous iteration. To disentangle the effects of intrinsic model improvements from those induced by dynamic external environments, this work proposes a three-dimensional evaluation framework—comprising learning capacity, data-driven potential, and knowledge retention—and, for the first time, decouples these dimensions into independent metrics. Through case studies simulating population distribution shifts, combined with dynamic performance tracking and knowledge retention measurements, the framework demonstrates that adaptive AI systems can balance learning and stability under gradual data evolution, while exhibiting a trade-off between plasticity and stability in abrupt change scenarios. This approach enables fine-grained, regulatory-grade assessment of the evolutionary trajectory of adaptive AI systems.

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.

Problem

• adaptive AI
• medical devices
• performance evaluation
• model updates
• dynamic environments

Innovation

• adaptive AI
• medical devices
• learning
• potential
• retention