Image by Gerd Altmann from Pixabay
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 
Download PDF
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.

-Read the full article through download.-
Do​wnload PDF

References

  1. Allen, S. Does Equity in Healthcare Use Vary across Canadian Provinces? Healthcare policy. 2008. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2645154/
  2. Automatic Processing Of Personal Data. Directorate General of Human Rights and Rule of Law. 2019. https://rm.coe.int/168063e391
  3. Barocas, S., Selbst AD., Big Data's Disparate Impact. SSRN. 2016. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899
  4. Brady M., Interoperability rule exposes gaps in protecting privacy. in Modern Healthcare 2020. https://www.modernhealthcare.com/information-technology/interoperability-rule-exposes-gaps-protecting-privacy
  5. Bresnick J., 4 Basics to Know about the Role of FHIR in Interoperability. in Health IT Analytics. 2016. https://healthitanalytics.com/news/4-basics-to-know-about-the-role-of-fhir-in-interoperability
  6. Callaghan, S., PIPEDA: What Canadian businesses need to know, 2018.https://www.cira.ca/blog/cybersecurity/pipeda-what-canadian-businesses-need-know
  7. Cattell, J., Chlukuri. S., Levy, M., How big data can revolutionize pharmaceutical R&D. Mckinsey & Company. 2013. https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d
  8. Consultation on the OPC’s Proposals for ensuring appropriate regulation of Artificial intelligence, Office of the privacy commissioner of Canada, 2020 https://www.priv.gc.ca/en/about-the-opc/what-we-do/consultations/consultation-ai/pos_ai_202001/
  9. Coos, A., PIPEDA vs GDPR: The Key Differences. 2019, Endprint protector. https://www.endpointprotector.com/blog/pipeda-vs-gdpr-the-key-differences/
  10. European Commission, European Electronic Health Record exchange format. 2019. https://ec.europa.eu/digital-single-market/en/news/recommendation-european-electronic-health-record-exchange-format
  11. Future AI Opportunities for Improving Care Delivery, Cost, and Efficacy, in Health IT Analytics 2019. https://healthitanalytics.com/news/future-ai-opportunities-for-improving-care-delivery-cost-and-efficacy
  12. Garnett, D. What Happened to the Distinction Between Primary and Secondary Research? 2019. https://douggarnett.com/uncategorized/ai-big-data-question-what-happened-to-the-distinction-between-primary-and-secondary-research/.
  13. General Data Protection Regulation. 2016. https://gdpr-info.eu
  14. Gibson, G., Innovation in healthcare depends on responsible, expanded data access for AI researchers, in The Globe and Mail 2020. https://www.theglobeandmail.com/opinion/article-innovation-in-health-care-depends-on-responsible-expanded-data-access/
  15. Goldman, B., A National Electronic Health Record For All Canadians, in CBC. 2019. https://www.cbc.ca/radio/whitecoat/a-national-electronic-health-record-for-all-canadians-1.4976932
  16. Government of Canada. Directive on Automated Decision-Making. 2019. https://www.tbs-sct.gc.ca/pol/doc-eng.aspx?id=32592
  17. Health Information Act. Office of the information and Privacy Commissioner of Alberta. 2001 https://www.alberta.ca/health-information-act.aspx
  18.  Institute of Medicine, Committee on Quality of Healthcare, Crossing the Quality Chasm: A New Health System for the 21st Century, 2001. https://pubmed.ncbi.nlm.nih.gov/25057539/
  19. Kostkova, P., H. Brewer, S. de Lusignan, E. Fottrell, B. Goldacre, G. Hart, P. Koczan P. Knight, C. Marsolier, R. A. McKendry, E. Ross, A. Sasse, R. Sullivan, S. Chaytor, O. Stevenson, R. Velho, and J. Tooke. "Who Owns the Data? Open Data for Healthcare." Front Public Health 4: 7. 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4756607/
  20. Langlois, S., Developing AI in a responsible way, in UDEM NOUVELLES 2018. https://phys.org/news/2018-12-ai-responsible.html
  21. Lehne, M., et al, Why digital medicine depends on interoperability. NPJ Digit Med, 2019. 2: p. 79. https://www.nature.com/articles/s41746-019-0158-1
  22. London, AJ., Artificial Intelligence and Black‐Box Medical Decisions: Accuracy versus Explainability. The Hastings Centre Report. 2019. https://onlinelibrary.wiley.com/doi/10.1002/hast.973
  23. Luxton, D.D., Artificial Intelligence in Behavioral and Mental Healthcare. 1 ed. 2016: Academic Press. https://www.sciencedirect.com/book/9780124202481/artificial-intelligence-in-behavioral-and-mental-health-care​
  24. Martin, N., Artificial Intelligence Is Being Used To Diagnose Disease And Design New Drugs, in Forbes. 2019. https://www.forbes.com/sites/nicolemartin1/2019/09/30/artificial-intelligence-is-being-used-to-diagnose-disease-and-design-new-drugs/
  25. Modernizing Canada’s Privacy Act. 2020. https://www.justice.gc.ca/eng/csj-sjc/pa-lprp/modern.html
  26. Montréal Declaration for a Responsible Development of Artificial Intelligence 2018. https://recherche.umontreal.ca/english/strategic-initiatives/montreal-declaration-for-a-responsible-ai/
  27. Murdoch, W. J., C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci USA116(44):22071-22080. 2019. https://www.pnas.org/content/116/44/22071
  28. News, S., SFU alumni company MetaOptima taps into Australian dermatology market, in SFU news 2018, SFU.4. https://www.sfu.ca/sfunews/stories/2018/07/sfu-alumni-company-metaoptima-taps-into-australian-dermatology.html
  29. O’Brien C., LifeLabs hack: Data protection 'a big problem for a lot of companies', in CTV News. 2019. https://www.ctvnews.ca/canada/lifelabs-hack-data-protection-a-big-problem-for-a-lot-of-companies-1.4748607
  30. O’Kane, J., AI startup enlisted for project to predict suicide risk in Canada, in The Globe and mail 2018. https://www.theglobeandmail.com/technology/government-enlists-ai-startup-for-project-to-predict-suicide-risk-in-canada/article37475839/
  31. Office of the Privacy Commissioner of Canada. Summary of privacy laws in Canada, Canada, Editor. 2018.https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/02_05_d_15/
  32. Panch, T., Mattie, H., Celi, LA., The “inconvenient truth” about AI in healthcare. Npj Digital Medicine. 2019. https://www.nature.com/articles/s41746-019-0155-4
  33. Rowe, J., How AI could help the doctor-patient relationship. AI Powered Healthcare, 2019. Modernizing Canada’s PrivacyAct.https://www.justice.gc.ca/eng/csj-sjc/pa-lprp/modern.html
  34. Siwicki, B., Northwell, UCSF, UNC using chatbot and related tech to manage COVID-19 patients, in Healthcare IT News. 2020. https://www.healthcareitnews.com/news/northwell-ucsf-unc-using-chatbot-and-related-tech-manage-covid-19-patients
  35. Steig, C., How this Canadian start-up spotted coronavirus before everyone else knew about it, in make it. 2020. https://www.cnbc.com/2020/03/03/bluedot-used-artificial-intelligence-to-predict-coronavirus-spread.html
  36. The Application of PIPEDA to Municipalities, Universities, Schools, and Hospitals, Office of the privacy commissioner of Canada, Canada, Editor. 2015. https://www.priv.gc.ca/en/privacy-topics/privacy-laws-in-canada/the-personal-information-protection-and-electronic-documents-act-pipeda/r_o_p/02_05_d_25/To W., Combes P., Leveraging AI and Machine Learning to Advance Interoperability in Healthcare. in Hit Consultant. 2020. https://hitconsultant.net/2020/01/15/leveraging-ai-and-machine-learning-to-advance-interoperability-in-healthcare/
  37. UNESCO. Canada first to adopt a strategy for artificial intelligence. 2018. http://www.unesco.org/new/en/media-services/single-view/news/canada_first_to_adopt_strategy_for_artificial_intelligence/Vogel, L. Rise of medical AI poses new legal risks for physicians. CMAJ. 2019. https://www.cmaj.ca/content/191/42/E1173
  38. Wakabayashi, D., Google and the University of Chicago Are Sued Over Data Sharing, in New York Times 2019. https://www.nytimes.com/2019/06/26/technology/google-university-chicago-data-sharing-lawsuit.html
  39. Walberg, R. Which provinces deliver the best health care. Frontier Center for Public library. 2008. https://fcpp.org/2008/09/17/which-provinces-deliver-the-best-healthcare/
  40. Wang F., Kaushal R., Khullar D., Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine?, Annals of Internal Medicine. 2020.https://www.acpjournals.org/doi/10.7326/M19-2548
  41. Wolff, J,. Pauling, J., Keck, A., Baumbach, J., The Economic Impact of Artificial Intelligence in Health Care: Systematic Review. Journal of Medical Internet Research. 2020. https://www.jmir.org/2020/2/e16866/
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: sumedhasachar@gmail.com
 
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: maia.dakessian@umontreal.ca
 
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: saina.beitari@mail.mcgill.ca
 
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: saishree.badrinarayanan@mail.mcgill.ca
 
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.

ISSN 2372-2193
© 2022 Journal of Science Policy & Governance, Inc. All rights reserved. The opinions, findings and conclusions from JSPG publications and events do not necessarily reflect the views of the journal.