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On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity

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Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging (CLIP 2023, EPIMI 2023, FAIMI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14242))

Abstract

Artificial intelligence (AI) holds tremendous promise for medical image analysis and computer-aided diagnosis, but various challenges must be addressed to enable its widespread adoption and impact in patient care. Open science, specifically through open-source code and public databases, brings multiple benefits to the progress of AI in medical imaging. It is expected to facilitate research output sharing, promote collaboration among researchers, improve the reproducibility of findings, and foster innovation. However, it is important to recognize that the current state of open-source research, particularly with respect to the large, public datasets commonly used in medical imaging AI, is inherently centered around high-income countries (HIC) and privileged populations. Low- and middle-income countries (LMIC) often face several limitations in contributing to and benefiting from open science research in this domain, such as inadequate digital infrastructure, limited funding for research and development, and a scarcity of healthcare and data science professionals. This may lead to further global disparities in health equity as AI-based clinical decision support systems continue to be implemented in practice. While transfer learning and distributed learning hold promise in addressing some challenges related to limited and non-public data in LMIC, practical obstacles arise when dealing with small, lower-quality datasets, resource constraints, and the need for tailored local implementation of these models. In this commentary, we explore the relationship between open-source models and public medical imaging data repositories in the context of transfer learning and distributed learning, specifically considering their implications for global health equity.

R. Souza and E.A.M. Stanley—Contributed equally.

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Author information

Authors and Affiliations

  1. Biomedical Engineering Graduate Program, University of Calgary, Calgary, Canada

    Raissa Souza, Emma A. M. Stanley & Nils D. Forkert

  2. Department of Radiology, University of Calgary, Calgary, Canada

    Raissa Souza, Emma A. M. Stanley & Nils D. Forkert

  3. Hotchkiss Brain Institute, University of Calgary, Calgary, Canada

    Raissa Souza, Emma A. M. Stanley & Nils D. Forkert

  4. Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, Canada

    Raissa Souza, Emma A. M. Stanley & Nils D. Forkert

  5. Department of Clinical Neurosciences, University of Calgary, Calgary, Canada

    Nils D. Forkert

Authors
  1. Raissa Souza
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  2. Emma A. M. Stanley
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  3. Nils D. Forkert
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Corresponding author

Correspondence to Emma A. M. Stanley .

Editor information

Editors and Affiliations

  1. Fraunhofer-Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Stefan Wesarg

  2. King's College London, London, UK

    Esther Puyol Antón

  3. Université de Rennes, Rennes, France

    John S. H. Baxter

  4. Singapore, Singapore

    Marius Erdt

  5. Aachen University of Applied Sciences, Aachen, Germany

    Klaus Drechsler

  6. Fraunhofer-Institute for Computer Graphics Research (IGD), Darmstadt, Germany

    Cristina Oyarzun Laura

  7. Technion – Israel Institute of Technology, Haifa, Israel

    Moti Freiman

  8. Tongji University, Shanghai, China

    Yufei Chen

  9. Imperial College London, London, UK

    Islem Rekik

  10. Western University, London, ON, Canada

    Roy Eagleson

  11. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  12. King's College London, London, UK

    Andrew P. King

  13. University of Copenhagen, Copenhagen, Denmark

    Veronika Cheplygina

  14. University of Copenhagen, Copenhagen, Denmark

    Melani Ganz-Benjaminsen

  15. Universidad Nacional del Litoral, Santa Fe, Argentina

    Enzo Ferrante

  16. Imperial College London, London, UK

    Ben Glocker

  17. Vanderbilt University, Nashville, TN, USA

    Daniel Moyer

  18. Technical University of Denmark, Kgs. Lyngby, Denmark

    Eikel Petersen

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Souza, R., Stanley, E.A.M., Forkert, N.D. (2023). On the Relationship Between Open Science in Artificial Intelligence for Medical Imaging and Global Health Equity. In: Wesarg, S., et al. Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging. CLIP EPIMI FAIMI 2023 2023 2023. Lecture Notes in Computer Science, vol 14242. Springer, Cham. https://doi.org/10.1007/978-3-031-45249-9_28

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  • DOI: https://doi.org/10.1007/978-3-031-45249-9_28

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