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Journal of Science Policy & Governance | Volume 17, Issue 01 | September 30, 2020

Policy Memo: ​Artificial Intelligence Alongside Physicians in Canada: Reality and Risks​​

Sumedha Sachar (1,2)*, Maïa Dakessian (1,3)*, Saina Beitari (1,4)*, Saishree Badrinarayanan (1,5)*
  1. Science & Policy Exchange Canada
  2. Department of Pharmacology, Université de Montréal, Monteal, QC
  3. Département de médecine sociale et préventive, Université de Montréal, Montreal, QC
  4. Department of Microbiology and Immunology, McGill University, Montreal, QC
  5. Integrated Program in Neuroscience, McGill University, Montreal, QC
​*All authors contributed equally 
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https://doi.org/10.38126/JSPG170119
Keywords: healthcare; artificial intelligence; machine learning; regulations; data privacy; interoperability; digital health; PIPEDA; GDPR​
Executive Summary: Artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize the healthcare system with their immense potential to diagnose, personalize treatments, and reduce physician burnout. These technologies are highly dependent on large datasets to learn from and require data sharing across organizations for reliable and efficient predictive analysis. However, adoption of AI/ML technologies will require policy imperatives to address the challenges of data privacy, accountability, and bias. To form a regulatory framework, we propose that algorithms should be interpretable and that companies that utilize a black box model for their algorithms be held accountable for the output of their ML systems. To aid in increasing accountability and reducing bias, physicians can be educated about the inherent bias that can be generated from the ML system. We further discuss the potential benefits and disadvantages of existing privacy standards ((Personal Information Protection and Electronic Documents Act) PIPEDA and (Personal Information Protection and Electronic Documents Act) GDPR) at the federal, provincial and territorial levels. We emphasize responsible implementation of AI by ethics, skill-building, and minimizing data privacy breaches while boosting innovation and increased accessibility and interoperability across provinces.

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Sumedha Sachar is a Postdoctoral fellow at University of Montreal, Department of Pharmacology. Her research focuses on liposome mediated miRNA delivery to Glioblastoma in mouse models. She is also a volunteer with non-profit organization Science and Policy Exchange.
Contact: [email protected]
 
Maïa Dakessian is a Master’s student in Bioethics, at the Université de Montréal, Département de médecine sociale et préventive. She is interested in neuroethics and how language is represented in the brain. She holds an undergraduate degree in Cognitive Neurosciences as well as one in Linguistics and Psychology, both from the Université de Montréal. For the past year, Maïa has been a volunteer with the non-profit organization Science & Policy Exchange. As the editor for the SPE Medium since December 2019, she coordinates writing ideas and projects to promote the importance of science in policy-making.
Contact: [email protected]
 
Saina Beitari is a graduate student at McGill University, department of Microbiology and Immunology. As a PhD student her research focused on studying the interaction between innate immunity and various viruses including HIV and SARS-COV-2. Saina is a volunteer with the non-profit organization Science & Policy Exchange. She is also a board member of AIDS Community Care Montreal.
Contact: [email protected]
 
Saishree Badrinarayanan is a PhD candidate in the Integrated Program in Neuroscience at McGill University. She holds a Master of Science from the University of Edinburgh in Integrative Neuroscience. Her research work focuses on the circuits responsible for spatial navigation and memory in mice. Saishree currently serves as treasurer for the student-led non-profit organization Science & Policy Exchange.
Contact: [email protected]
 
Acknowledgements
The authors would like to thank Dr. Sam Garnett, Anh-Khoi Trinh and Shawn McGuirk from Science & Policy Exchange for comments and suggestions during the development of this policy memo. They would also like to thank their editors Linda D. Ho and Derek Wang for feedback during the revisions.
 
Disclaimer
The authors declare no conflict of interest.
DISCLAIMER: The findings and conclusions published herein are solely attributed to the author and not necessarily endorsed or adopted by the Journal of Science Policy and Governance. Articles are distributed in compliance with copyright and trademark agreements.

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