A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning
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DOI：
10.1080/15265161.2021.2013977
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发表时间：
2022-01-19
影响因子：
13.4
通讯作者：
Shaul, Randi Zlotnik
中科院分区：
文献类型：
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作者：
McCradden, Melissa D.;Anderson, James A.;Shaul, Randi Zlotnik
人工智能和机器学习（ML）技术在医疗保健中的应用具有改善患者护理的巨大潜力。虽然围绕负责任的ML和监管框架出现了一些新的实践，但研究伦理监督的传统作用在其与临床ML的相关性方面相对未被探索。在本文中，我们提供了一个全面的研究伦理框架，可以应用于ML研究在其开发周期中的系统查询。该路径由三个阶段组成：（1）探索性、产生假设的数据访问;（2）静默期评估;（3）前瞻性临床评估。我们将每个阶段与其文献和伦理理由联系起来，并建议对传统范式进行调整，以适应ML，同时保持伦理严谨性和对个人的保护。该途径可以容纳从观察到对照试验的多种研究设计，并且这些阶段可以单独应用于各种ML应用。
The application of artificial intelligence and machine learning (ML) technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surrounding responsible ML as well as regulatory frameworks, the traditional role of research ethics oversight has been relatively unexplored regarding its relevance for clinical ML. In this paper, we provide a comprehensive research ethics framework that can apply to the systematic inquiry of ML research across its development cycle. The pathway consists of three stages: (1) exploratory, hypothesis-generating data access; (2) silent period evaluation; (3) prospective clinical evaluation. We connect each stage to its literature and ethical justification and suggest adaptations to traditional paradigms to suit ML while maintaining ethical rigor and the protection of individuals. This pathway can accommodate a multitude of research designs from observational to controlled trials, and the stages can apply individually to a variety of ML applications.