利用可解释AI测量跨司法管辖区医疗设备风险概念的转移
Measuring Cross-Jurisdictional Transfer of Medical Device Risk Concepts with Explainable AI
AI总结：
研究通过可解释AI探讨跨司法管辖区医疗设备风险分类的可转移性，发现分类信号微弱且不对称，挑战了跨司法管辖区监管AI的常见假设。
摘要：
美国FDA、中国NMPA和欧洲EU MDR均使用风险语言，但分类机制结构不同。本文通过将可解释AI作为实证工具，测试跨司法管辖区监管重叠的可转移性。利用141,942个设备记录，推导出七个EU MDR风险因素，包括植入性、侵入性和使用时长，并在三乘三的转移矩阵中评估其贡献。在对称提取管道下，所有司法管辖区的因子贡献均微不足道，表明跨司法管辖区信号至多是边缘性的。在特定司法管辖区管道下，仅在EU MDR到NMPA方向有小幅提升，但敏感性分析显示该效果弱、情境依赖且部分受提取和表示选择影响。反向方向探测显示强不对称性：FDA推导的因子在任何方向均无法转移，NMPA推导的因子无法回传到EU MDR。零样本转移在EU MDR I类上失败，与残余和位置类定义不匹配一致。总体而言，跨司法管辖区转移稀疏、不对称且微弱。共享的监管词汇在此操作化下并未转化为强可移植的分类逻辑。研究挑战了跨司法管辖区监管AI的常见假设，并展示了可解释AI如何用于测量而非假设监管重叠。
英文摘要：
Medical device regulators in the United States(FDA), China (NMPA), and Europe (EU MDR) all use the language of risk, but classify devices through structurally different mechanisms. Whether these apparently shared concepts carry transferable classificatory signal across jurisdictions remains unclear. We test this by reframing explainable AI as an empirical probe of cross-jurisdictional regulatory overlap. Using 141,942 device records, we derive seven EU MDR risk factors, including implantability, invasiveness, and duration of use, and evaluate their contribution across a three-by-three transfer matrix. Under a symmetric extraction pipeline designed to remove jurisdiction-specific advantages, factor contribution is negligible in all jurisdictions, indicating that clean cross-jurisdictional signal is at most marginal. Under jurisdiction specific pipelines, a modest gain appears only in the EU MDR-to-NMPA direction, but sensitivity analyses show that this effect is weak, context-dependent, and partly confounded by extraction and representation choices. Reverse direction probes show strong asymmetry: FDA-derived factors do not transfer meaningfully in any direction, and NMPA-derived factors do not carry signal back to EU MDR. Zero-shot transfer further fails on EU MDR Class I, consistent with a mismatch between residual and positional class definitions. Overall, cross-jurisdictional transfer is sparse, asymmetric, and weak. Shared regulatory vocabulary does not, under this operationalisation, translate into strong portable classification logic. The findings challenge a common assumption in cross-jurisdictional regulatory AI and show how explainable AI can be used to measure, rather than assume, regulatory overlap.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
