Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms. We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.

翻译：医学图像分析是一个充满活力的研究领域,它为医生和医生提供了宝贵的洞察力和准确诊断及监测疾病的能力。机器学习为这个领域提供了额外的推动力。然而,医学图像分析的机器学习特别容易受到自然偏差的影响,如影响算法性能和稳健性的域变。在本文中,我们分析在技术戒备水平框架内进行医学图像分析的机器学习,并审查在创建强大和适应性强的医疗图像分析算法时,因果关系分析方法如何填补空白。我们利用医学成像AI/ML中的因果关系来审查方法,发现因果分析有可能减轻临床翻译的关键问题,但迄今为止,吸收和临床下游研究有限。