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Artificial Intelligence, Medical Devices, Canada, Healthcare, Machine Learning, AI Regulation


Artificial Intelligence (AI) may transform Canadian healthcare. The hope is that AI will enable more accurate and efficient care, thereby solving many access, quality, and safety problems. Yet, despite this tantalizing prospect, there are risks of unsafe AI harming patients, algorithmic bias, and threats to privacy. This work begins analysis of whether applicable Canadian laws are up to the task of ensuring Canadians can benefit from effective health-related AI while minimizing AI-related risks. It focuses on Health Canada’s regulation of medical devices, a ‘first line of defence’ that decides which devices are safe, effective, and thus permitted for trade in Canadian markets. After highlighting the regulatory challenge, we provide the first detailed explanation of Canadian medical device regulations and how they apply to AI-enabled devices. We then discuss a still-developing “alternative pathway” for licencing devices with AI and the regulatory gaps left open. We conclude with recommendations that a recent emphasis on post-market surveillance should not be at the expense of robust pre-market review and that safety and efficacy review embrace bias- and privacy-related risks. Further, whilst post-market surveillance holds potential for ensuring the safety of adaptive machine-learning medical devices over time, much will depend on regulatory capacity and competency and investments therein.


Funding Information: This paper is part of the Canadian Institutes of Health Research (CIHR)-funded (grant 155390) project Machine MD: How Should We Regulate AI in Health Care?, co-led by Colleen M. Flood, Anna Goldenberg, Catherine Régis, and Teresa Scassa.

Declaration of Interests: Matthew Herder reported being a member of the Patented Medicine Prices Review Board, Canada’s national drug price regulator, and receiving honoraria for his service. Michael Da Silva was a nonremunerated member of Health Canada’s External Reference Group on Adaptive Machine Learning-Enabled Medical Devices.