Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all sources of biases in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess how those biases are encoded in models, and how capable bias mitigation methods are at ameliorating performance disparities. In this article, we introduce a novel analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. We developed and tested this framework for conducting controlled in silico trials to assess bias in medical imaging AI using a tool for generating synthetic magnetic resonance images with known disease effects and sources of bias. The feasibility is showcased by using three counterfactual bias scenarios to measure the impact of simulated bias effects on a convolutional neural network (CNN) classifier and the efficacy of three bias mitigation strategies. The analysis revealed that the simulated biases resulted in expected subgroup performance disparities when the CNN was trained on the synthetic datasets. Moreover, reweighing was identified as the most successful bias mitigation strategy for this setup, and we demonstrated how explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Developing fair AI models is a considerable challenge given that many and often unknown sources of biases can be present in medical imaging datasets. In this work, we present a novel methodology to objectively study the impact of biases and mitigation strategies on deep learning pipelines, which can support the development of clinical AI that is robust and responsible.

翻译：针对临床任务使用医学影像训练的人工智能模型常表现出不同亚组间性能差异的偏见形式。由于现实世界医学影像数据中的偏见源并非全部易于识别，全面评估这些偏见如何编码于模型中以及偏见缓解方法能在多大程度上改善性能差异具有挑战性。本文提出一种新颖的分析框架，用于系统客观地研究医学影像中偏见对人工智能模型的影响。我们开发并测试了该框架，通过使用能够生成包含已知疾病效应与偏见源的合成磁共振图像工具，开展受控的计算机模拟试验以评估医学影像人工智能的偏见。通过采用三种反事实偏见场景来衡量模拟偏见效应对卷积神经网络分类器的影响及三种偏见缓解策略的有效性，展示了该框架的可行性。分析表明，当卷积神经网络在合成数据集上训练时，模拟偏见导致了预期的亚组性能差异。此外，重加权被确认为该实验设置中最有效的偏见缓解策略，并展示了可解释人工智能方法如何在此框架下辅助探究偏见在模型中的表现形式。鉴于医学影像数据集中可能存在众多且常属未知的偏见源，开发公平的人工智能模型面临重大挑战。本研究提出了一种创新方法，能够客观研究偏见及缓解策略对深度学习流程的影响，可为开发稳健可靠的临床人工智能提供支持。