利用机器学习解决外科护理中的种族差异问题。

Addressing racial disparities in surgical care with machine learning.

作者信息

Halamka John, Bydon Mohamad, Cerrato Paul, Bhagra Anjali

机构信息

Mayo Clinic, Rochester, MN, USA.

出版信息

NPJ Digit Med. 2022 Sep 30;5(1):152. doi: 10.1038/s41746-022-00695-6.

Abstract

There is ample evidence to demonstrate that discrimination against several population subgroups interferes with their ability to receive optimal surgical care. This bias can take many forms, including limited access to medical services, poor quality of care, and inadequate insurance coverage. While such inequalities will require numerous cultural, ethical, and sociological solutions, artificial intelligence-based algorithms may help address the problem by detecting bias in the data sets currently being used to make medical decisions. However, such AI-based solutions are only in early development. The purpose of this commentary is to serve as a call to action to encourage investigators and funding agencies to invest in the development of these digital tools.

摘要

有充分的证据表明,对几个人口亚群体的歧视会影响他们获得最佳手术治疗的能力。这种偏见可能有多种形式,包括获得医疗服务的机会有限、护理质量差以及保险覆盖不足。虽然这种不平等需要众多文化、伦理和社会学方面的解决方案,但基于人工智能的算法可能有助于解决这一问题,即通过检测当前用于医疗决策的数据集中的偏见。然而,这种基于人工智能的解决方案仍处于早期开发阶段。本评论的目的是呼吁采取行动,鼓励研究人员和资助机构投资于这些数字工具的开发。

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