An AI A Day Keeps The Doctor Away?: Regulating Artificial Intelligence In Healthcare

• Published date: 09 May 2022
• Subject Matter: Employment and HR, Food, Drugs, Healthcare, Life Sciences, Privacy, Discrimination, Disability & Sexual Harassment, Data Protection, Privacy Protection, Biotechnology & Nanotechnology
• Law Firm: McCarthy Tétrault LLP
• Author: TechLex Blog, Dana Siddle, Jennifer K. Choi and Ellen Yifan Chen

Artificial Intelligence ("AI") promises to transform many aspects of everyday life for Canadians. AI tools are predicted to dramatically improve the provision of heath care by improving the quality, safety, and efficiency of diagnostic tools, treatment decisions, and care. Although AI innovations are, in many cases, still years away from general deployment into the Canadian health care ecosystem, AI is already used in some circumstances to read medical images, allowing machine learning to support diagnosticians in their decision-making.

Like many other jurisdictions, Canada's health governance systems currently lack the appropriate legal and regulatory mechanisms to effectively deal with the challenges that AI poses. There is currently uncertainty with respect to key issues such as the related legal requirements for health privacy, medical device regulation and liability for AI-related harms. In Canada, regulation of AI in health care involves the additional challenge of navigating constitutionally fragmented jurisdiction over health care, which results in layers of governance and the need to coordinate multiple different actors.

This blog post highlights some of the legal challenges and issues that need to be addressed in order for Canada to have a robust and well-regulated governance structure for the use of AI in health care, including:

• Coordination of federal and provincial authority;
• Privacy and oversight with respect to the use of AI in treatment;
• Promotion of Equity through AI; and
• Liability for AI-related harms.

Coordination of Federal and Provincial Authority

Canada's federal system and constitutional division of powers pose unique challenges for the regulation of AI in health care.¹ Under the Constitution, health care is under provincial jurisdiction. Although similar, each province has its own set of regulatory frameworks addressing the safety and quality of health care, health information privacy, informed consent, human rights and non-discrimination, and licensing of health care professionals. With respect to the adoption of AI, provincial legislation and regulation will be the primary legal structure that governs the end users of AI technology and its application to patients.

However, despite health care being primarily a provincial concern, the federal government plays a significant role, particularly through its spending powers under the Canada Health Act,² and its responsibilities for Indigenous Peoples, federal prisoners, and the military. The federal government is also a significant player in the regulation of drugs and medical devices.

Health Canada is the key regulatory authority at the federal level that controls which medical devices are available for sale and may be included in the public insurance plans of the provinces and territories. Health Canada's primary mode of regulation is through the licensing process applicable to all medical devices. This licensing process requires manufacturers to classify their devices according to risk (e.g., invasiveness, risk of erroneous diagnosis, and intended medical purpose) under the Medical Devices Regulations,³ and obtain approval from Health Canada. If a device is licensed, the Medical Device Directorate continues to monitor the safety and efficacy of the device.⁴

A significant challenge for the licensing and regulation of AI-technology is the application of machine learning, which is often referred to as "black-box" decision making, because the relevant algorithms are often proprietary and commercially sensitive and decisions and impacts of the algorithms cannot be fully explained. A question that is being asked by regulators around the world is "how can a regulator verify and validate machine learning algorithms to ensure that they do what they say well and safely?"⁵ Another question is: what...