Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Journal: Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Published Date: Apr 5, 2019

Abstract

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.

Authors

  • Jacob L Jaremko
    Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada.
  • Marleine Azar
    Department of Medicine, Université de Montréal, Montréal, Quebec, Canada.
  • Rebecca Bromwich
    Department of Law and Legal Studies, Carleton University, Ottawa, Canada.
  • Andrea Lum
    Dept. of Medical Imaging, Schulich School of Medicine and Dentistry, University of Western Ontario, 1151 Richmond St, London, ON, Canada.
  • Li Hsia Alicia Cheong
    Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
  • Martin Gibert
    Centre de recherche en éthique, Université de Montréal, Montréal, Quebec, Canada.
  • François Laviolette
    Department of Computer Science and Software Engineering, Université Laval, Québec, Canada.
  • Bruce Gray
  • Caroline Reinhold
    Department of Radiology, McGill University Health Center, Montréal, Québec, Canada.
  • Mark Cicero
    From the *Department of Medical Imaging, St Michael's Hospital, and †Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada.
  • Jaron Chong
    Department of Radiology, McGill University Health Center, Montréal, Québec, Canada.
  • James Shaw
    Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada.
  • Frank J Rybicki
    From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (T.C., A.A.G., K.K.K., F.J.R., D.M.); Harvard T.H. Chan School of Public Health, Boston, Mass (S.Y.); and Department of Radiology, Brigham and Women's Hospital, Boston, Mass (T.K., B.R.).
  • Casey Hurrell
    Canadian Association of Radiologists, Ottawa, Ontario, Canada.
  • Emil Lee
    Canadian Association of Radiologists, Ottawa, Ontario, Canada; Department of Radiology, Valley Medical Imaging, Langley, British Columbia, Canada; Department of Medical Imaging, Fraser Health Authority, British Columbia, Canada.
  • An Tang
    Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montreal, Canada.

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