In this study, musculoskeletal radiologists were asked to evaluate two sets of scan results per patient, including one from a standard MR exam and one from the fastMRI model, and were not told which one was created using AI. The images were examined for meniscal tears, ligament abnormalities, and cartilage defects, and also graded for quality. Both sets delivered the same results and led to the same diagnoses, but the radiologists said they preferred the AI image quality over the traditional images.

Going forward, the researchers said they aim to make MR scans 10x faster and plan to test FastMRI on other organs, such as the brain. They’ve also published all FastMRI data, models, and code as an open-access project to allow other researchers and MR manufacturers to build on and participate in the work, which they hope will soon lead clinical implementation.


“Rather than using existing images to train AI algorithms, we will radically change the way medical images are acquired in the first place,” Dr. Daniel K. Sodickson, director of NYU's Center for Advanced Imaging Innovation and Research, said in a statement. “Our aim is not merely to enhance data mining with AI, but rather create new capabilities for medical visualization, to benefit human health.”

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