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精准医学社会责任使用的系统决策框架。

Systematic decision frameworks for the socially responsible use of precision medicine.

作者信息

Peebles Ian S, Kinney David B, Foster-Hanson Emily

机构信息

School of Historical, Philosophical and Religious Studies, Arizona State University, Tempe, AZ, USA.

Department of Philosophy and Program in Philosophy-Neuroscience-Psychology, Washington University in St. Louis, St. Louis, MO, USA.

出版信息

NPJ Genom Med. 2024 Oct 5;9(1):46. doi: 10.1038/s41525-024-00433-9.

Abstract

Deep learning techniques and whole-genome sequencing promise to increase well-being but also risk perpetuating psychological essentialism, potentially justifying inequality. In this Comment, we offer two much-needed systematic frameworks for clinicians and researchers to avoid essentialist inferences and unfair treatment: (1) a data-driven method for detecting causal fairness in precision health and (2) an ethical framework for determining when it is morally permissible to use racial classifications in population health research.

摘要

深度学习技术和全基因组测序有望增进福祉,但也有延续心理本质主义的风险,这可能会使不平等现象合理化。在这篇评论文章中,我们为临床医生和研究人员提供了两个急需的系统框架,以避免本质主义推断和不公平待遇:(1)一种用于检测精准医疗中因果公平性的数据驱动方法,以及(2)一个用于确定在人群健康研究中何时在道德上允许使用种族分类的伦理框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8c0/11455903/8c6c146ef8d0/41525_2024_433_Fig1_HTML.jpg

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