That AI scanning your X-ray for signs of COVID-19 may just be looking at your age

In brief Machines are like humans – they're lazy. When given the chance to take the easy route to complete an easy task, they will. Academics at the University of Washington found that algorithms trained to diagnose COVID-19 from chest X-rays often look at secondary features, such as a patient's age, rather than focusing on the images themselves – something known as shortcut learning. "A physician would generally expect a finding of COVID-19 from an X-ray to be based on specific patterns in the image that reflect disease processes," said Alex DeGrave, a medical science student at the American university and co-author of a paper published this week in Nature Intelligence. "But, rather than relying on those patterns, a system using shortcut learning might, for example, judge that someone is elderly and, thus, infer that they are more likely to have the disease because it is more common in older patients. The shortcut is not wrong per se, but the association is unexpected and not transparent. And, that could lead to an inappropriate diagnosis."