Medical Xpress November 25, 2024
In a new review, Yale researchers provide an in-depth analysis of how biases at different stages of AI development can lead to poor clinical outcomes and exacerbate health disparities. The authors say their results reflect an old adage in the computing world: “Garbage in, garbage out.”
“Bias in, bias out,” said John Onofrey, Ph.D., assistant professor of radiology & biomedical imaging and of urology at Yale School of Medicine (YSM) and senior author of the study. “The same idea absolutely applies.”
Published November 7 in PLOS Digital Health, the article provides examples, both hypothetical and real, to illustrate how bias impacts health care outcomes and provide mitigation strategies.
“Having worked in the machine learning/AI field for many years now, the...
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