Regulatory Challenges in AI/ML-Enabled Medical Devices: A Scoping Review and Conceptual Framework

Abstract

Abstract Amidst rapid advancements in artificial intelligence and machine learning-enabled medical devices (AI/ML-MD), this article investigates the regulatory challenges highlighted in the current academic literature. Using a PRISMA-guided scoping review, 18 studies were selected for in-depth analysis to highlight the multifaceted issues in regulating AI/ML-MD. The study's findings are organized into key themes: adaptive AI/ML, usability and stakeholder engagement, data diversity and use, health disparities, synthetic data use, regulatory considerations, medicolegal issues, and cybersecurity threats. The scoping review reveals numerous challenges associated with the regulation of AI/ML-based medical devices, reflecting various sustainability pillars. The study advocates for integrating sustainability principles into the materiovigilance ecosystem of AI/ML-MD and proposes a novel sustainable ecosystem for AI/ML-MD materiovigilance. This proposed ecosystem incorporates social, economic, and environmental sustainability principles to create a comprehensive and balanced regulatory approach. By presenting a thorough analysis of regulatory challenges, the study provides policymakers with a nuanced understanding of the complex landscape surrounding these technologies. This insight enables the development of informed strategies and solutions to address regulatory gaps and ensure the safe and effective integration of AI/ML-MD into healthcare systems.

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