Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health

被引：0

作者：

Hurd, Thelma C. ^{[1
,2
]}

Payton, Fay Cobb ^{[3
,4
]}

Hood, Darryl B. ^{[5
]}

机构：

[1] Meharry Med Coll, Inst Hlth Dispar Equ & Exposome, Nashville, TN USA

[2] Univ Calif Merced, Sch Social Sci Humanities & Arts, Merced, CA USA

[3] Rutgers Univ Newark, Sch Arts & Sci, Newark, NJ USA

[4] North Carolina State Univ, Raleigh, NC USA

[5] Ohio State Univ, Coll Publ Hlth, Columbus, OH USA

来源：

MILBANK QUARTERLY | 2024年

关键词：

algorithmic bias; minority health; artificial intelligence; machine learning; ethics; CLINICAL-TRIALS; DISPARITIES; CHALLENGES; OPPORTUNITIES; MODEL; AI;

D O I：

10.1111/1468-0009.12712

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in health care and regulate its application in legal and ethical ways. Significant progress has been made in governance in the United States and the European Union. It is incumbent on developers, end users, the public, providers, health care systems, and policymakers to collaboratively ensure that we adopt a national AI health strategy that realizes the Quintuple Aim; minimizes race-based medicine; prioritizes transparency, equity, and algorithmic vigilance; and integrates the patient and community voices throughout all aspects of AI development and deployment.

引用

页数：28

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