Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence.

Journal: Pediatric radiology
Published Date: Jul 6, 2022

Abstract

The integration of human and machine intelligence promises to profoundly change the practice of medicine. The rapidly increasing adoption of artificial intelligence (AI) solutions highlights its potential to streamline physician work and optimize clinical decision-making, also in the field of pediatric radiology. Large imaging databases are necessary for training, validating and testing these algorithms. To better promote data accessibility in multi-institutional AI-enabled radiologic research, these databases centralize the large volumes of data required to effect accurate models and outcome predictions. However, such undertakings must consider the sensitivity of patient information and therefore utilize requisite data governance measures to safeguard data privacy and security, to recognize and mitigate the effects of bias and to promote ethical use. In this article we define data stewardship and data governance, review their key considerations and applicability to radiologic research in the pediatric context, and consider the associated best practices along with the ramifications of poorly executed data governance. We summarize several adaptable data governance frameworks and describe strategies for their implementation in the form of distributed and centralized approaches to data management.

Authors

  • Suranna R Monah
    Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Ave., Toronto, ON, M5G 1X8, Canada. suranna.monah@sickkids.ca.
  • Matthias W Wagner
    Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland.
  • Asthik Biswas
    Division of Neuroradiology, The Hospital for Sick Children, Toronto, Canada.
  • Farzad Khalvati
    Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada. farzad.khalvati@utoronto.ca.
  • Lauren E Erdman
    Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
  • Afsaneh Amirabadi
    Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada; Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada.
  • Logi Vidarsson
    Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Ave., Toronto, ON, M5G 1X8, Canada.
  • Melissa D McCradden
    Division of Neurosurgery (McCradden, Baba, Saha, Boparai, Fadaiefard, Cusimano), St. Michael's Hospital, Unity Health Toronto; Dalla Lana School of Public Health (Cusimano), University of Toronto, Toronto, Ont. injuryprevention@smh.ca.
  • Birgit B Ertl-Wagner
    From the Department of Radiology, University of Wisconsin Madison School of Medicine and Public Health, 600 Highland Dr, Madison, WI 53792 (D.A.B., M.L.S.); Department of Radiology, New York University, New York, NY (L.M.); Department of Musculoskeletal Radiology (M.A.B.) and Institute for Technology Assessment (E.F.H.), Massachusetts General Hospital, Boston, Mass; Department of Medical Imaging, Hospital for Sick Children, University of Toronto, Toronto, Canada (B.B.E.W.); Department of Radiology, University of California-San Diego, San Diego, Calif (K.J.F.); Department of Cancer Imaging, Division of Imaging Sciences & Biomedical Engineering, Kings College London, London, England (V.J.G.); Department of Radiology and Biomedical Imaging, University of California-San Francisco, San Francisco, Calif (C.P.H.); and Department of Radiology and Radiologic Science, The Johns Hopkins University School of Medicine, Baltimore, Md (C.R.W.).

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