CANADA Trends and Developments Contributed by: Martin Lapner, Vanessa Carroll, Taryn C Burnett and Robert Sheahan, Gowling WLG (Canada) LLP
Intellectual Property Uncertainties Canadian intellectual property statutes have not yet expressly addressed ownership or infringement ques - tions concerning AI-generated content, including its application in healthcare. For example, questions remain regarding the subsistence of copyright, author - ship attribution, and inventorship for patent-eligible AI outputs. Responsible AI integration in health-related research and medical report generation is required to safeguard against plagiarism and protect intellectual property. Conclusion AI promises to revolutionise Canadian healthcare delivery, diagnostics, and resource allocation. While the regulatory landscape continues to evolve, liability and risk management considerations for healthcare providers and organisations, as well as AI vendors and developers, favour a cautious approach.
well as the right of individuals to request a human decision-maker. Regulatory Initiatives and Investigations Federal and provincial Privacy Commissioners have been among the most active in developing expecta - tions for the use of AI, including in the healthcare sec - tor. While they do not directly regulate AI as a whole, they play a key role in ensuring that the use of AI sys - tems aligns with existing privacy laws (eg, Personal Information Protection and Electronic Documents Act (PIPEDA) and provincial health privacy statutes). The Office of the Privacy Commissioner of Canada (OPC) published A Regulatory Framework for AI: Recommendations for PIPEDA Reform, which rec - ommends stronger accountability, more explicit rules for automated decision-making, and rights for indi - viduals to challenge AI-driven decisions. For the first time, both provincial and federal Commissioners have engaged in investigating AI-related privacy concerns, including working collaboratively to coordinate AI oversight in a joint investigation. Algorithmic Bias and Discrimination There may be unconscious bias and unintentional discrimination in the training data used to develop AI systems, which can extend historic harms in the form of biased output. Academic literature, including a 2024 Stanford-led study, has demonstrated that large language model chatbots can perpetuate debunked, racially biased medical myths. AI tools that recom - mend discriminatory practices, whether unintentional or not, could form the basis of a human rights claim.
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