Machine learning-enabled medical imaging analysis has become a vital part of the automatic diagnosis system. However, machine learning, especially deep learning models have been shown to demonstrate a systematic bias towards certain subgroups of people. For instance, they yield a preferential predictive performance to males over females, which is unfair and potentially harmful especially in healthcare scenarios. In this literature survey, we give a comprehensive review of the current progress of fairness studies in medical image analysis (MedIA) and healthcare. Specifically, we first discuss the definitions of fairness, the source of unfairness and potential solutions. Then, we discuss current research on fairness for MedIA categorized by fairness evaluation and unfairness mitigation. Furthermore, we conduct extensive experiments to evaluate the fairness of different medical imaging tasks. Finally, we discuss the challenges and future directions in developing fair MedIA and healthcare applications

翻译：基于机器学习的医学影像分析已成为自动诊断系统中的重要组成部分。然而，机器学习模型，尤其是深度学习模型，已被证明对某些人群存在系统性偏见。例如，它们在预测性能上对男性优于女性，这种不公平性在医疗场景中尤其具有潜在危害。在这篇文献综述中，我们全面回顾了医学图像分析（MedIA）与医疗领域公平性研究的当前进展。具体而言，我们首先讨论了公平性的定义、不公平性的来源以及潜在解决方案。接着，我们按公平性评估与不公平性缓解的分类，探讨了MedIA公平性的当前研究。此外，我们开展了大量实验以评估不同医学影像任务的公平性。最后，我们讨论了开发公平的MedIA与医疗应用所面临的挑战与未来方向。