Bibb Allen, MD, FACR, chief medical officer of the American College of Radiology (ACR) Data Science Institute, explains the trend of using AI for the automated detection of stroke on computed tomography (CT) imaging and the need to include radiologists on the stroke care team.

“Our approach has the potential to facilitate timely diagnoses and management of pancreatic lesions encountered in routine clinical practice,” experts involved in the study said.

A fully automated body composition analysis derived from CT imaging can be a valuable pretreatment tool for patients with colorectal cancer. 

In a close collaboration with radiation oncologists, experts trained their model on the CT lung images of 787 patients and tested it on the scans of more than 1,300 patients from external datasets.

To promote the legitimacy of their products, companies most often tout their partnerships with medical and academic institutions, in addition to their applications’ legal approvals.

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.