Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review.

Journal: Journal of medical Internet research
PMID: 39773888

Abstract

BACKGROUND: Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals' personal or protected attributes.

Authors

  • Maxime Sasseville
    Faculté des sciences infirmières, Université Laval, Québec, QC, Canada.
  • Steven Ouellet
    Faculté des sciences infirmières, Université Laval, Québec, QC, Canada.
  • Caroline Rhéaume
    Vitam Research Center on Sustainable Health, Québec, QC, Canada.
  • Malek Sahlia
    École Nationale des Sciences de l'Informatique, Université de La Manouba, La Manouba, Tunisia.
  • Vincent Couture
    Faculty of Nursing, Université Laval, Québec, QC, Canada.
  • Philippe Després
    Faculty of Science and Engineering, Department of Physics, Physical Engineering and Optics, Université Laval, Quebec, QC, Canada.
  • Jean-Sébastien Paquette
    Vitam Research Center on Sustainable Health, Québec, QC, Canada.
  • David Darmon
    Risques, Epidémiologie, Territoires, Informations, Education et Santé. Département d'enseignement et de recherche en médecine générale, Université Côte d'Azur, Nice, France.
  • Frédéric Bergeron
    Direction des services-conseils de la Bibliothèque, Université Laval, Québec, QC, Canada.
  • Marie-Pierre Gagnon
    Research Center on Healthcare and Services in Primary Care, Université Laval, Quebec, QC, Canada.

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