Aligning XAI with EU Regulations for Smart Biomedical Devices: A Methodology for Compliance Analysis

EU regulations require different levels of AI transparency depending on device type: The study categorizes medical devices by their control mechanisms (open-loop, closed-loop, and semi-closed-loop systems) and shows that each category has distinct explainability requirements under EU law, meaning a one-size-fits-all approach to AI transparency in medical devices is insufficient.

Specific XAI methods should be matched to regulatory requirements, not chosen arbitrarily: Rather than selecting explainability techniques based solely on technical performance, the research demonstrates a methodology for matching particular XAI algorithms to the specific explanation goals mandated by EU regulations for different medical applications.

Neural implants and advanced prosthetics require tailored transparency approaches: The paper's case studies on neural implants for chronic disease management and prosthetics reveal that high-risk bioelectronic devices need customized explainability strategies, highlighting that medical device complexity demands proportionate transparency measures.

Current AI opacity in medical devices creates regulatory compliance gaps: The study identifies that the "black box" nature of many AI systems in healthcare conflicts with EU regulatory requirements for transparency and trustworthiness, suggesting many deployed medical AI systems may not meet legal standards for explainability.

Bridging XAI and regulation requires systematic methodology: The research provides a structured framework for healthcare developers and regulators to align technical AI explanation capabilities with legal requirements, addressing a previously unresolved gap between AI research and medical device compliance.