Diverging regulatory DNA in adaptive medical AI: US agility and EU accountability in lifecycle governance

Abstract

Medical artificial intelligence (AI) is transitioning from static, rule-based systems into adaptive models capable of continuous learning and iterative refinement. Such adaptivity expands the utility and performance of clinical AI systems across diverse patient populations and real-world conditions. However, these properties challenge regulatory paradigms originally designed for fixed-function medical devices. Although the United States and the European Union share goals of ensuring safety, accountability, and trustworthy performance, their regulatory architectures diverge due to underlying legal-philosophical traditions. The United States employs a common-law, evidence-driven approach centered on the Total Product Life Cycle, using predetermined change-control mechanisms and real-world observational data to support iterative improvement under controlled risk. In contrast, the European Union adopts a civil-law, precautionary model operationalized through the Artificial Intelligence Act, the Medical Device Regulation, and the revised Product Liability Directive, emphasizing ex-ante duties, transparency, traceability, and accountability. Understanding these distinct regulatory DNAs is critical for aligning lifecycle governance of adaptive AI across jurisdictions and ensuring safe, context-responsive innovation.

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