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.
Department of Medicine, Montreal Heart Institute, Montreal, Quebec, Canada.
Can J Cardiol. 2024 Oct;40(10):1907-1921. doi: 10.1016/j.cjca.2024.04.026. Epub 2024 May 11.
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.
在医学人工智能（AI）的动态领域中，心脏病学因其技术进步和临床应用而成为一个关键领域。在这篇综述中，我们探讨了数据偏差这一复杂问题，特别是在心脏病学中开发和实施 AI 工具时所遇到的数据偏差。我们剖析了这些偏差的起源和影响，这些偏差挑战了它们在医疗保健中的可靠性和广泛适用性。通过一个案例研究，我们从临床角度强调了处理这些偏差所涉及的复杂性。本综述的目标是为研究人员和临床医生提供识别、理解和减轻这些偏差所需的实用知识，倡导创建不仅在技术上合理，而且对所有患者公平有效的 AI 解决方案。
