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 E-Mail Artificial intelligence promises to be a powerful tool for improving the speed and accuracy of medical decision-making to improve patient outcomes. From diagnosing disease, to personalizing treatment, to predicting complications from surgery, AI could become as integral to patient care in the future as imaging and laboratory tests are today. But as University of Washington researchers discovered, AI models like humans have a tendency to look for shortcuts. In the case of AI-assisted disease detection, these shortcuts could lead to diagnostic errors if deployed in clinical settings. In a new paper published May 31 in Nature Machine Intelligence, UW researchers examined multiple models recently put forward as potential tools for accurately detecting COVID-19 from chest radiography, otherwise known as chest X-rays. The team found that, rather than learning genuine medical pathology, these models rely instead on shortcut learning to draw spurious associations between me

Gabriel erionPascal sturmfelsScott lundbergAlex degraveJoseph janizekMicrosoft researchUniversity of washingtonUw medical scientist training programNational institutes of healthNational science foundationAllen schoolNature machine intelligenceComputer scienceScientist trainingSu in leeNature machine