https://www.sciencedaily.com/releases/2020/05/200512134541.htm

A team of researchers, led by the University of Cambridge and Simon Fraser University, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad artefacts, or unwanted alterations in the data, among other major errors in the final images. The effects were typically not present in non-AI based imaging techniques.

The phenomenon was widespread across different types of artificial neural networks, suggesting that the problem will not be easily remedied. The researchers caution that relying on AI-based image reconstruction techniques to make diagnoses and determine treatment could ultimately do harm to patients. Their results are reported in the Proceedings of the National Academy of Sciences.