提出三维度评估自适应医疗AI模型，区分性能变化来源。
Learning, Potential, and Retention: An Approach for Evaluating Adaptive AI-Enabled Medical Devices
- 用学习、潜力、保留三指标分离模型改进与环境变化影响
- 模拟人群迁移实验显示渐进变化更利于稳定性能
- 适合监管机构评估迭代AI医疗设备的安全有效性
本文针对自适应人工智能医疗设备评估难题，提出一种新方法，包含三个互补度量：学习（模型在当前数据上的提升）、潜力（数据集驱动的性能变化）、保留（跨修改步骤的知识保持），以区分模型适应与动态环境导致的性能变化。基于模拟人群迁移的案例研究显示，渐进式转变有助于稳定的学习与保留，而快速变化则暴露可塑性与稳定性之间的权衡。该方法为监管科学提供实用洞见，支持对自适应AI系统在连续更新下的安全性和有效性进行严谨评估。
原文摘要 · Abstract (English)
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.
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