Yale study shows how AI bias worsens healthcare disparities

The research shows how data integrity issues at every stage – training, model development, publication, implementation – can adversely impact patient outcomes, say clinicians at Yale School of Medicine.

A new research report from Yale School of Medicine offers an up-close look at how biased artificial intelligence can affect clinical outcomes. The study focuses specifically on the different stages of AI model development, and shows how data integrity issues can impact health equity and care quality.

WHY IT MATTERS
Published earlier this month in PLOS Digital Health, the research gives both real-world and hypothetical illustrations of how AI bias impacts adversely affects healthcare delivery – not just at the point of care, but at every stage of medical AI development: training data, model development, publication and implementation.

"Bias in; bias out," said the study's senior author, John Onofrey, assistant professor of radiology & biomedical imaging and of urology at Yale School of Medicine, in a press statement.

"Having worked in the machine learning/AI field for many years now, the idea that bias exists in algorithms is not surprising," he said. "However, listing all the potential ways bias can enter the AI learning process is incredible. This makes bias mitigation seem like a daunting task."

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