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

Pediatric Radiology volume 52, pages 2111–2119 (2022)Cite this article

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.

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Authors and Affiliations

  1. Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Ave., Toronto, ON, M5G 1X8, Canada

    Suranna R. Monah, Matthias W. Wagner, Farzad Khalvati, Afsaneh Amirabadi, Logi Vidarsson & Birgit B. Ertl-Wagner

  2. Great Ormond Street Hospital, London, UK

    Asthik Biswas

  3. Centre for Computational Medicine, The Hospital for Sick Children, Toronto, ON, Canada

    Lauren E. Erdman

  4. Department of Computer Science, University of Toronto, Toronto, ON, Canada

    Lauren E. Erdman

  5. Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada

    Melissa D. McCradden

  6. Division of Clinical & Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

    Melissa D. McCradden

  7. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

    Birgit B. Ertl-Wagner

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  1. Suranna R. Monah
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  2. Matthias W. Wagner
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Correspondence to Suranna R. Monah.

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Monah, S.R., Wagner, M.W., Biswas, A. et al. Data governance functions to support responsible data stewardship in pediatric radiology research studies using artificial intelligence. Pediatr Radiol 52, 2111–2119 (2022). https://doi.org/10.1007/s00247-022-05427-2

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  • DOI: https://doi.org/10.1007/s00247-022-05427-2

Keywords

  • Artificial intelligence
  • Centralized networks
  • Data governance
  • Data integrity
  • Data stewardship
  • Distributed networks
  • Pediatric radiology
  • Radiology
  • Research