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医学专业

BMJ人工智能研究指导

为了患者利益的机器学习和人工智能研究:关于透明度、可复现性、伦理、有效性的20个关键问题

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.

机器学习,人工智能和其他现代统计方法为先前未开发且快速增长的数据源提供了新的利用机会,从而为患者带来了利益。尽管目前正在进行许多有希望的研究,尤其是在影像学方面,但总体上文献缺乏透明度,缺乏促进可重复性的清晰报告,缺乏对潜在的道德问题的探索,以及缺乏明确的有效性证明。这些问题存在的众多原因中,最重要的一个(现在我们提供了一个初步的解决方案)是目前缺少针对机器学习和人工智能的最佳实践指南。但是,我们相信,涉及健康领域机器学习和人工智能研究和影响该类项目的跨学科的团队,将会从明确一系列有关透明度,可复现性,伦理和有效性(TREE)的问题中受益。这里提出的20个关键问题,为研究团队发布研究设计,实施和报告,为编辑和同行评议者评估文献的贡献,以及让患者、临床医生和政策制定者严格评估新发现可能在哪些方面为患者带来收益,提供了一个框架。

BMJ 2020; 368 doi: https://doi.org/10.1136/bmj.l6927  (Published 20 March 2020)

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