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.
 

Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America, explains considerations for healthcare IT teams on the implementation of artificial intelligence (AI).

As artificial intelligence (AI) adoption expands in radiology, there is growing concern that AI algorithms need to undergo quality assurance (QA) reviews.

Bibb Allen, MD, FACR, Chief Medical Officer of the American College of Radiology (ACR) Data Science Institute, and former ACR president, explains how hospitals or radiology departments can conduct quality assurance assessments on artificial intelligence algorithms they adopt to ensure they are accurate. 

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains the state of AI in radiology in 2022.