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.
摘要：
在医学人工智能（AI）的动态领域，心脏病学是其技术进步和临床应用的关键领域。这篇综述探讨了复杂的数据偏差问题，专门解决在心脏病人工智能工具的开发和实施过程中遇到的问题。我们剖析这些偏见的起源和影响，这挑战了它们在医疗保健中的可靠性和广泛适用性。使用案例研究，我们强调了从临床角度解决这些偏见所涉及的复杂性.这篇综述的目的是让研究人员和临床医生掌握识别所需的实践知识，理解,减轻这些偏见，倡导创造不仅仅是技术上合理的人工智能解决方案，而且对所有患者的人口统计学也是公平有效的。
