The digitization of healthcare data coupled with advances in computational capabilities has propelled the adoption of machine learning (ML) in healthcare. However, these methods can perpetuate or even exacerbate existing disparities, leading to fairness concerns such as the unequal distribution of resources and diagnostic inaccuracies among different demographic groups. Addressing these fairness problem is paramount to prevent further entrenchment of social injustices. In this survey, we analyze the intersection of fairness in machine learning and healthcare disparities. We adopt a framework based on the principles of distributive justice to categorize fairness concerns into two distinct classes: equal allocation and equal performance. We provide a critical review of the associated fairness metrics from a machine learning standpoint and examine biases and mitigation strategies across the stages of the ML lifecycle, discussing the relationship between biases and their countermeasures. The paper concludes with a discussion on the pressing challenges that remain unaddressed in ensuring fairness in healthcare ML, and proposes several new research directions that hold promise for developing ethical and equitable ML applications in healthcare.

翻译：医疗数据的数字化与计算能力的进步推动了机器学习在医疗领域的广泛应用。然而，这些方法可能延续甚至加剧现有的不平等现象，导致公平性问题，例如不同人群之间资源分配不均和诊断准确性差异。解决这些公平问题对于防止社会不公正的进一步固化至关重要。在本综述中，我们分析了机器学习公平性与医疗保健差异的交叉领域。我们基于分配正义原则构建了一个框架，将公平性问题分为两类：平等分配与平等绩效。我们从机器学习的角度对相关的公平性指标进行了批判性评述，审视了机器学习生命周期各阶段的偏差及其缓解策略，并讨论了偏差与相应应对措施之间的关系。本文最后讨论了确保医疗机器学习公平性方面尚未解决的紧迫挑战，并提出了若干有前景的新研究方向，以推动医疗领域伦理与公平的机器学习应用发展。