Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology.

Journal: The Canadian journal of cardiology
Published Date:

Abstract

In the dynamic field of medical artificial intelligence (AI), cardiology stands out as a key area for its technological advancements and clinical application. In this review we explore the complex issue of data bias, specifically addressing those encountered during the development and implementation of AI tools in cardiology. We dissect the origins and effects of these biases, which challenge their reliability and widespread applicability in health care. Using a case study, we highlight the complexities involved in addressing these biases from a clinical viewpoint. The goal of this review is to equip researchers and clinicians with the practical knowledge needed to identify, understand, and mitigate these biases, advocating for the creation of AI solutions that are not just technologically sound, but also fair and effective for all patients.

Authors

  • Alexis Nolin-Lapalme
    Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada; Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada; Mila - Québec AI Institute, Montreal, Quebec, Canada; Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Quebec, Canada. Electronic address: alexis.nolin-lapalme@umontreal.ca.
  • Denis Corbin
    Laboratoire d'Imagerie optique et Moléculaire, Polytechnique Montréal, 2500 Chemin de Polytechnique Montréal, Montreal, QC, H3T 1J4, Canada. denis.corbin@hotmail.com.
  • Olivier Tastet
    Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada.
  • Robert Avram
    Division of Cardiology, Department of Medicine, Montreal Heart Institute, University of Montreal, Montreal, QC H1T 1C8, Canada. Electronic address: robert.avram.md@gmail.com.
  • Julie G Hussin
    Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada; Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada; Mila - Québec AI Institute, Montreal, Quebec, Canada.