The automation of bias in medical Artificial Intelligence (AI): Decoding the past to create a better future

被引:38
|
作者
Straw, Isabel [1 ,2 ]
机构
[1] Univ Penn, Perelman Sch Med, Philadelphia, PA 19104 USA
[2] Univ Penn, Perelman Sch Med, Dept Publ Hlth, Philadelphia, PA 19104 USA
关键词
Disparities; Inequality; Data science; Bias; Health; Medicine; Digital health; Artificial intelligence; Healthcare; GENDER; DIAGNOSIS;
D O I
10.1016/j.artmed.2020.101965
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence (AI) into the healthcare field the future care of our patients will depend on the decisions we make now. Demographic healthcare inequalities continue to persist worldwide and the impact of medical biases on different patient groups is still being uncovered by the research community. At a time when clinical AI systems are scaled up in response to the Covid19 pandemic, the role of AI in exacerbating health disparities must be critically reviewed. For AI to account for the past and build a better future, we must first unpack the present and create a new baseline on which to develop these tools. The means by which we move forwards will determine whether we project existing inequity into the future, or whether we reflect on what we hold to be true and challenge ourselves to be better. AI is an opportunity and a mirror for all disciplines to improve their impact on society and for medicine the stakes could not be higher.
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页数:3
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