Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness
Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness
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DOI：
10.1136/bmj.l6927
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发表时间：
2020-03-20
影响因子：
105.7
通讯作者：
Hemingway, Harry
中科院分区：
文献类型：
--
作者：
Vollmer, Sebastian;Mateen, Bilal A.;Hemingway, Harry
机器学习、人工智能和其他现代统计方法正在提供新的机会，将以前未开发和快速增长的数据源用于患者利益。尽管目前正在进行许多有希望的研究，特别是在成像方面，但作为一个整体，文献缺乏透明度，缺乏便于复制的清晰报告，缺乏对潜在伦理问题的探索，缺乏有效性的明确展示。在这些问题存在的众多原因中，最重要的原因之一(我们在这里提供了初步解决方案)是目前缺乏专门针对机器学习和人工智能的最佳实践指导。然而，我们认为，寻求涉及机器学习和人工智能健康的研究和影响项目的跨学科小组将受益于明确解决一系列关于透明度、可重复性、道德和有效性(TREE)的问题。这里提出的20个关键问题为研究小组提供了一个框架，以便为设计、实施和报告提供信息；供编辑和同行评审员评估对文献的贡献；为患者、临床医生和政策制定者提供一个框架，以批判性地评估新发现可能带来患者利益的地方。
Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.