With the software’s help, the ratio of CTs requiring radiologist review to missed findings identified was 10:1, experts shared, adding that without the help of AI that ratio would be at least 66:1. 

Denoising using deep learning techniques can boost the performance of 1.5T MR brain imaging, resulting in quality comparable or superior to 3T imaging. 

To date, research pertaining to reactive lymphadenopathy has focused mostly on patients with cancer who are routinely staged and monitored via PET/CT or in women undergoing breast cancer screening and/or imaging with mammography and ultrasound.

“We found that if you tune the classical methods, they can perform very well,” an expert involved in the research said.

In an effort to perhaps dissuade skepticism among medical students who are on the fence about the future of radiologists, experts in the field recently offered a detailed overview of the use of AI in imaging.

In particular, the experts paid close attention to instances of extrathyroidal extension (ETE) on ultrasound. 

A new artificial intelligence system can detect active tuberculosis on chest radiographs with accuracy comparable to radiologists, a recent paper in Radiology reports. 

A new analysis offers a detailed comparison of soft-tissue lymphomas and soft-tissue tumors based on imaging characteristics from MRI scans—an area of study that has not yet been rigorously explored, the authors of the paper indicated.