Building equitable artificial intelligence in health care: addressing current challenges and exploring future opportunities

Building equitable artificial intelligence in health care: addressing current challenges and exploring future opportunities

Collection:
Health Policy and Services Research
Author(s):
Zink, Anna L., author
Morriss, Sarah, (Of Urban Institute), author
Gangopadhyaya, Anuj, author
Obermeyer, Ziad, author
Contributor(s):
Health Policy Center (Urban Institute), issuing body.
Publication:
Washington, DC : Urban Institute, September 2023
Language(s):
English
Format:
Text
Subject(s):
Artificial Intelligence -- ethics
Health Equity
Health Policy
Healthcare Disparities
Prejudice -- prevention & control
United States
Genre(s):
Technical Report
Abstract:
AI applications in health care are prone to biases that could perpetuate health disparities. In this paper we study the ways in which AI may maintain, perpetuate, or worsen inequitable outcomes in health care. We review current approaches to evaluating and mitigating biased AI and potential applications of AI to address health equities. Finally, we discuss current incentives for equitable AI and potential changes in the regulation and policy space. As AI becomes increasingly embedded in the daily operations of health care systems, it is imperative that we understand its risks and evaluate its impact on health equity.
Copyright:
Reproduced with permission of the copyright holder. Further use of the material is subject to CC BY-NC-DC license. (More information)
Extent:
1 online resource (1 PDF file (20 pages))
NLM Unique ID:
9918734183106676 (See catalog record)
Permanent Link:
http://resource.nlm.nih.gov/9918734183106676