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The promise of data science for health research in Africa

Author

Listed:
  • Clement A. Adebamowo

    (University of Maryland School of Medicine
    Center for Bioethics and Research)

  • Shawneequa Callier

    (The George Washington University
    National Institutes of Health)

  • Simisola Akintola

    (Center for Bioethics and Research
    University of Ibadan
    University of Ibadan)

  • Oluchi Maduka

    (Center for Bioethics and Research)

  • Ayodele Jegede

    (Center for Bioethics and Research
    University of Ibadan
    University of Ibadan)

  • Christopher Arima

    (Syracuse University College of Law)

  • Temidayo Ogundiran

    (Center for Bioethics and Research
    University of Ibadan
    University of Ibadan)

  • Sally N. Adebamowo

    (University of Maryland School of Medicine
    Center for Bioethics and Research)

Abstract

Data science health research promises tremendous benefits for African populations, but its implementation is fraught with substantial ethical governance risks that could thwart the delivery of these anticipated benefits. We discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.

Suggested Citation

  • Clement A. Adebamowo & Shawneequa Callier & Simisola Akintola & Oluchi Maduka & Ayodele Jegede & Christopher Arima & Temidayo Ogundiran & Sally N. Adebamowo, 2023. "The promise of data science for health research in Africa," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41809-2
    DOI: 10.1038/s41467-023-41809-2
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    References listed on IDEAS

    as
    1. Morley, Jessica & Machado, Caio C.V. & Burr, Christopher & Cowls, Josh & Joshi, Indra & Taddeo, Mariarosaria & Floridi, Luciano, 2020. "The ethics of AI in health care: A mapping review," Social Science & Medicine, Elsevier, vol. 260(C).
    2. Nima Kordzadeh & Maryam Ghasemaghaei, 2022. "Algorithmic bias: review, synthesis, and future research directions," European Journal of Information Systems, Taylor & Francis Journals, vol. 31(3), pages 388-409, May.
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