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Nolin-Lapalme A, Corbin D, Tastet O, Avram R, Hussin JG. Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology. Can J Cardiol 2024; 40:1907-1921. [PMID: 38735528 DOI: 10.1016/j.cjca.2024.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2024] Open
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.
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Affiliation(s)
- 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.
| | - Denis Corbin
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Olivier Tastet
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada
| | - Robert Avram
- Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada; Faculté de Médecine, Université de Montréal, Montreal, Quebec, Canada; Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Quebec, Canada
| | - 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
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Gard KE, Dries D, House C. Performing Accurate Standard 12 Lead ECGs on patients with Burns to the Chest. Air Med J 2024; 43:8-10. [PMID: 38154846 DOI: 10.1016/j.amj.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/01/2023] [Indexed: 12/30/2023]
Abstract
The use of the electrocardiogram (ECG) in critical care settings is a long-established cardiovascular monitoring tool. The effectiveness of the routine 12-lead ECG relies on accurate lead placement that is consistent and replicable. Improper lead placement may display erroneous ECG patterns and affect patient management decisions.1,2 In the setting of an acute injury, such as a torso burn to the ventral surface, accurate lead placement may be compromised or impossible. The regional burn center, which is part of our organization, sees approximately 500 patients per year. Of those patients, burns to the chest accounted for 21% of admissions during 2020 and 2021. This significant fraction of burn injury patients requires modification of our standard approach to provide an accurate ECG. Baseline ECGs are routinely acquired on the burn unit per protocol and for monitoring of patient response to numerous pharmaceutical therapies.
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