AAMI and BSI (the U.K. national standards body and international standards developing organization) have teamed up to explore the ways that artificial intelligence (AI) and machine learning pose unique challenges to the standards and regulations that govern medical devices and related technologies.
The resulting new position paper, Machine Learning AI in Medical Devices: Adapting Regulatory Frameworks and Standards to Ensure Safety and Performance, is available for free download. The position paper examines how machine learning is different from traditional medical devices and medical software.
“Machine learning products and applications have aided clinical practice and contributed to more accessible, efficient, and effective healthcare. However, as machine learning amplifies AI capabilities, numerous concerns arise,” said Joe Lewelling, senior standards advisor at AAMI. “This position paper, developed in collaboration with more than 30 AI and machine learning experts in the U.S. and the U.K., provides recommendations for additional guidance or standards that might be needed to promote the safety and efficacy of medical technologies.”
Ad Statistics
Times Displayed: 57043
Times Visited: 752 Reveal Mobi Pro integrates the Reveal 35C detector with SpectralDR technology into a modern mobile X-ray solution. Mobi Pro allows for simultaneous acquisition of conventional & dual-energy images with a single exposure. Contact us for a demo at no cost.
AI mimics intelligent human behavior, processing, and analyzing data at a scale impossible for humans to do on their own. Some AI technologies make diagnoses or deliver therapy, using static, rules-based systems and established clinical protocols. Machine learning takes AI a leap further, continuously learning, making predictions and judgments, and, in some systems, modifying algorithms autonomously.
“This group shared very pragmatic, boots-on-the-ground challenges,” said Pat Baird, senior regulatory specialist and head of global software standards at Philips, who co-authored the position paper. “Data quality and applicability are very essential—the old ‘garbage-in, garbage-out’ rule—so data quality will be a key success factor.”
The following recommendations are made in Machine Learning AI in Medical Devices:
Developing, in collaboration with International Medical Device Regulators Forum (IMDRF) and other regulatory bodies, standardized critical terminology and a taxonomy for medical AI that can inform future national and regional regulatory approaches to the technology.
That IMDRF establish an AI working group to address issues around AI in healthcare and to prepare needed guidance and good regulatory practices.
Mapping AI-applicable international regulatory standards (where such exist) to the October 2018 IMDRF Essential Principles and identifying gaps where additional new standards or guidance are needed.
