The value of standards for health datasets in artificial intelligence-based applications.
The value of standards for health datasets in artificial intelligence-based applications.
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DOI：
10.1038/s41591-023-02608-w
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发表时间：
2023-11
期刊：
影响因子：
82.9
通讯作者：
Liu, Xiaoxuan
中科院分区：
文献类型：
--
作者：
Arora, Anmol;Alderman, Joseph E.;Palmer, Joanne;Ganapathi, Shaswath;Laws, Elinor;Mccradden, Melissa D.;Oakden-Rayner, Lauren;Pfohl, Stephen R.;Ghassemi, Marzyeh;Mckay, Francis;Treanor, Darren;Rostamzadeh, Negar;Mateen, Bilal;Gath, Jacqui;Adebajo, Adewole O.;Kuku, Stephanie;Matin, Rubeta;Heller, Katherine;Sapey, Elizabeth;Sebire, Neil J.;Cole-Lewis, Heather;Calvert, Melanie;Denniston, Alastair;Liu, Xiaoxuan
人工智能作为一种医疗设备，正越来越多地应用于医疗诊断、风险分层和资源分配。然而，越来越多的证据强调了算法偏见的风险，这可能会使现有的健康不平等永久化。出现这个问题的部分原因是数据集管理方面的系统性不平等，参与研究的机会不平等以及访问的不平等。本研究旨在探讨确保卫生数据集充分的数据多样性的现有标准、框架和最佳做法。探索现有文献和专家意见的主体是制定基于共识的指南的重要一步。该研究包括两个部分：对医疗数据集的现有标准、框架和最佳实践进行系统性审查;对利益相关者对偏见、健康公平和人工智能作为医疗器械的最佳实践的看法进行调查和专题分析。我们发现，文献中对数据集多样性的需求进行了很好的描述，专家们普遍赞成制定一套强大的指南，但对于如何实际实施这些指南，人们的看法不一。这项研究的成果将用于为卫生数据集数据多样性透明度标准的制定提供信息（共同立场倡议）。一项系统性综述，结合利益相关者调查，概述了健康数据集管理的当前实践和建议，特别关注数据多样性和基于人工智能的应用。
Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative). A systematic review, combined with a stakeholder survey, presents an overview of current practices and recommendations for dataset curation in health, with specific focuses on data diversity and artificial intelligence-based applications.
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影响因子：
3.7
作者：
Floridi, Luciano;Cowls, Josh;Taddeo, Mariarosaria
通讯作者：
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DOI：
10.1093/jamia/ocaa144
发表时间：
2020-11-01
影响因子：
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发表时间：
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期刊：
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作者：
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通讯作者：
Fields S
DOI：
10.1080/15265161.2020.1819469
发表时间：
2020-11
期刊：
The American journal of bioethics : AJOB
影响因子：
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作者：
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通讯作者：
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DOI：
10.1038/s41431-020-00741-5
发表时间：
2020-12
期刊：
European journal of human genetics : EJHG
影响因子：
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作者：
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