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.

Julius Bogdan, vice president and general manager of the Healthcare Information and Management Systems Society (HIMSS) Digital Health Advisory Team for North America, explains several key artificial intelligence (AI) trends he sees across healthcare.

Bibb Allen, MD, FACR, chief medical officer of the American College of Radiology (ACR) Data Science Institute, discusses multiple factors involved in the adoption rate of artificial intelligence in radiology.