While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice. A notable concern is the potential to exacerbate entrenched biases and existing health disparities in society. The area of fairness in machine learning seeks to address these issues of equity; however, appropriate approaches are context-dependent, necessitating domain-specific consideration. We focus on clinical trials, i.e., research studies conducted on humans to evaluate medical treatments. Clinical trials are a relatively under-explored application in machine learning for healthcare, in part due to complex ethical, legal, and regulatory requirements and high costs. Our aim is to provide a multi-disciplinary assessment of how fairness for machine learning fits into the context of clinical trials research and practice. We start by reviewing the current ethical considerations and guidelines for clinical trials and examine their relationship with common definitions of fairness in machine learning. We examine potential sources of unfairness in clinical trials, providing concrete examples, and discuss the role machine learning might play in either mitigating potential biases or exacerbating them when applied without care. Particular focus is given to adaptive clinical trials, which may employ machine learning. Finally, we highlight concepts that require further investigation and development, and emphasize new approaches to fairness that may be relevant to the design of clinical trials.
翻译:虽然近年来对应用机器学习来改善保健的兴趣大大增加,但有一些障碍阻止了医疗实践的部署。一个值得注意的问题是,有可能加剧根深蒂固的偏见和社会上现有的健康差异。机器学习的公平性领域寻求解决这些公平问题;然而,适当的方法取决于背景,需要针对具体领域的考虑。我们注重临床试验,即对人体的研究,以评价医疗治疗。临床试验是治疗机学习中相对探索不足的应用,部分原因是复杂的道德、法律和监管要求以及高昂的费用。我们的目标是对机器学习的公平性如何适应临床试验研究和实践进行多学科评估。我们首先审查临床试验的现行道德考虑和准则,并审查它们与机器学习公平性的共同定义之间的关系。我们研究临床试验中不公平的潜在根源,提供具体的例子,并讨论机器学习在不加注意地应用时在减轻潜在偏差或加剧这种偏差方面可能发挥的作用。我们特别注重适应性临床试验,这可能利用机器学习的新的公平性来进行。我们首先审查目前的临床试验的道德考虑和准则,并审查它们与机器学习中公平性的共同定义之间的关系。我们研究临床试验的潜在来源,提供具体的例子,并讨论机器学习在不加注意地应用时可能起到作用。我们强调临床研究的革新性概念,以便强调新的研究。我们强调临床试验需要进行新的研究。