A new study highlights a hidden challenge of using AI in medical imaging research -- the phenomenon of highly accurate yet potentially misleading results known as 'shortcut learning.
A new study highlights a hidden challenge of using AI in medical imaging research -- the phenomenon of highly accurate yet potentially misleading results known as 'shortcut learning.' The researchers analyzed thousands of knee X-rays and found that AI models can 'predict' unrelated and implausible traits such as whether patients abstained from eating refried beans or beer.
"While AI has the potential to transform medical imaging, we must be cautious," says the study's senior author, Dr. Peter Schilling, an orthopaedic surgeon at Dartmouth Health's Dartmouth Hitchcock Medical Center and an assistant professor of orthopaedics in Dartmouth's Geisel School of Medicine. "This goes beyond bias from clues of race or gender," says Brandon Hill, a co-author of the study and a machine learning scientist at Dartmouth Hitchcock."We found the algorithm could even learn to predict the year an X-ray was taken. It's pernicious -- when you prevent it from learning one of these elements, it will instead learn another it previously ignored.
"AI is almost like dealing with an alien intelligence," Hill continues."You want to say the model is 'cheating,' but that anthropomorphizes the technology. It learned a way to solve the task given to it, but not necessarily how a person would. It doesn't have logic or reasoning as we typically understand it."
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