Original article was published on artificial intelligence
Artificial intelligence-based techniques, used to reconstruct medical images, may actually be leading to incorrect diagnoses.
That’s according to the results of a new investigation, led by experts at the University of Cambridge. Scientists there devised a series of tests to assess such imaging reconstruction and discovered numerous artefacts and other errors, according to their study, published May 11 in the Proceedings of the National Academy of Sciences.
This issue seemed to persist across different types of AI, they noted, and may not be easily remedied.
“There’s been a lot of enthusiasm about AI in medical imaging, and it may well have the potential to revolutionize modern medicine; however, there are potential pitfalls that must not be ignored,” co-author Anders Hansen, PhD, from Cambridge’s Department of Applied Mathematics and Theoretical Physics, said in a statement. “We’ve found that AI techniques are highly unstable in medical imaging, so that small changes in the input may result in big changes in the output.”
To reach their conclusions, Hansen and coinvestigators—from Norway, Portugal, Canada and the United Kingdom—used several assessments to pinpoint flaws in AI algorithms. They targeted CT, MR and nuclear magnetic resonance imaging, and tested them based on instabilities tied to movement, small structural changes, and those related to the number of samples.