Optimizing human-centered AI for healthcare in the Global South

被引:13
作者
Okolo, Chinasa T. [1 ]
机构
[1] Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA
来源
PATTERNS | 2022年 / 3卷 / 02期
关键词
NEURAL-NETWORK; TUBERCULOSIS; COVID-19; TREND;
D O I
10.1016/j.patter.2021.100421
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Over the past 60 years, artificial intelligence (AI) has made significant progress, but most of its benefits have failed to make a significant impact within the Global South. Current practices that have led to biased systems will prevent AI from being actualized unless significant efforts are made to change them. As technical advances in AI and an interest in solving new problems lead researchers and tech companies to develop AI applications that target the health of marginalized communities, it is crucially important to study how AI can be used to empower those on the front lines in the Global South and how these tools can be optimally designed for marginalized communities. This perspective examines the landscape of AI for healthcare in the Global South and the evaluations of such systems and provides tangible recommendations for AI practitioners and human-centered researchers to incorporate in the development of AI systems for use with marginalized populations.
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收藏
页数:7
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