Measuring Cross-Jurisdictional Transfer of Medical Device Risk Concepts with Explainable AI

TL;DR

This study uses explainable AI to empirically assess whether medical device risk concepts are transferable across US, Chinese, and European regulations, finding limited and asymmetric transferability.

Contribution

It demonstrates how explainable AI can measure actual regulatory overlap, revealing weak and asymmetric transfer of risk concepts across jurisdictions.

Findings

Cross-jurisdictional risk signals are negligible under symmetric extraction.

Modest transfer observed from EU MDR to NMPA, but weak and context-dependent.

FDA and NMPA risk factors do not transfer meaningfully to other jurisdictions.

Abstract

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.