{Reference Type}: Journal Article
{Title}: Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology.
{Author}: Nolin-Lapalme A;Corbin D;Tastet O;Avram R;Hussin JG;
{Journal}: Can J Cardiol
{Volume}: 0
{Issue}: 0
{Year}: 2024 May 11
{Factor}: 6.614
{DOI}: 10.1016/j.cjca.2024.04.026
{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.