AI in medical devices: regulatory challenges and the path forward

Abstract

Abstract Purpose Artificial Intelligence (AI) and Machine Learning (ML) are increasingly deployed as Software as a Medical Device (SaMD), including adaptive systems whose performance evolves through continuous learning. While these technologies offer clinical and operational benefits, their dynamic nature challenges traditional regulatory frameworks designed for static medical products. This article aims to critically examine the regulatory implications of adaptive AI/ML-based medical devices and to identify key governance gaps within existing oversight models. Methods A critical-integrative analysis of the current regulatory landscape was conducted, focusing on major frameworks and guidance documents governing AI-enabled medical devices. The analysis examines regulatory approaches such as the United States Food and Drug Administration’s Total Product Lifecycle framework, Predetermined Change Control Plans, and emerging Good Machine Learning Practice principles, alongside international harmonization initiatives. Key regulatory challenges—including continuous learning, post-market monitoring, algorithmic bias, transparency, cybersecurity, and accountability—were systematically evaluated. Results The analysis identifies operational and governance gaps in existing regulatory approaches, in regulating continuously learning systems across their lifecycle. While lifecycle-oriented and risk-based frameworks represent important advances, current models remain insufficiently operationalized for adaptive AI systems. Challenges related to post-market performance surveillance, real-world data integration, transparency, and international regulatory alignment remain unresolved. Conclusions Rather than proposing a wholly new regulatory framework, this study outlines concrete operational mechanisms for strengthening governance of adaptive AI/ML medical devices. These include risk-tiered, lifecycle-oriented oversight, enhanced post-market surveillance mechanisms, AI-specific technical standards, and sustained multi-stakeholder collaboration. Addressing these gaps is essential to ensure the safe and trustworthy integration of adaptive AI into healthcare.

Ähnliche Arbeiten