Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms.

Journal: Journal of clinical epidemiology
PMID: 39532254

Abstract

BACKGROUND AND OBJECTIVES: Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The extent to which bias is present, the groups who most frequently experience bias, and the mechanism through which bias is introduced in clinical ML applications is not well described. The objective of this study was to examine instances of bias in clinical ML models. We identified the sociodemographic subgroups PROGRESS that experienced bias and the reported mechanisms of bias introduction.

Authors

  • Michael Colacci
    St. Michael's Hospital (Verma, Colacci, Bell, Ailon, Friedrich, Kuzulugil, Yang, Lee, Pou-Prom, Mamdani), Unity Health Toronto; Department of Medicine (Verma, Colacci, Ailon, Friedrich, Lee, Mamdani), and Institute of Health Policy, Management, and Evaluation (Verma, Stukel, Colacci, Murray, Mamdani), and Department of Laboratory Medicine and Pathobiology (Verma, Mamdani), University of Toronto; ICES Central (Stukel); Leslie Dan Faculty of Pharmacy (Mamdani), University of Toronto, Toronto, Ont.
  • Yu Qing Huang
    St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
  • Gemma Postill
    Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
  • Pavel Zhelnov
    St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
  • Orna Fennelly
    St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
  • Amol Verma
    St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
  • Sharon Straus
    Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.
  • Andrea C Tricco
    Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 209 Victoria St, Toronto, Ontario, M5B 1T8, Canada. Andrea.Tricco@unityhealth.to.

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