Researchers have used a new ultrasound-based imaging technique, vector flow imaging, on pediatric patients for the first time, publishing their findings in Progress in Pediatric Cardiology.

A new AI model can accurately determine a patient’s five-year cancer risk based on a single breast MR image, outperforming state-of-the art risk assessment models.

An AI platform designed to quickly read an x-ray and determine the manufacturer and model of a cardiac rhythm device may quicken treatment in the event of device failure.

“Organized medicine, including national radiology specialty societies, will need to evaluate this trend and impact on society membership," wrote authors of a recent study published in the Journal of the American College of Radiology.

The bipartisan bill (HR 1969) was introduced into the U.S. House of Representatives Tuesday, April 2, by Reps. Danny Davis (D-IL) and Brad Wenstrup (R-OH) and would provide Medicare coverage for CT colonography screening.

What happens when a software virus runs up against a physician trained to spot ailments? In a recent study, malware designed to create fake nodules on images successfully fooled radiologists into making incorrect diagnoses.

The algorithm was externally validated on 486 normal chest radiographs and 529 abnormal chest radiographs taken from four different institutions across multiple continents.

The FDA announced Tuesday, April 3, that it is working on a new framework to regulate AI-based medical devices that continually learn from healthcare data.

Amyloid PET imaging greatly influenced the clinical management of patients with mild cognitive impairment (MCI) and dementia, according to the first phase of a multicenter trial published April 2 in JAMA.

In women 65 and older, digital breast tomosynthesis (DBT) achieved a higher specificity for detecting breast cancer and identified the disease at an earlier stage compared to traditional 2D mammography.

The American College of Radiology (ACR) published its latest updated Appropriateness Criteria to help radiologists improve care across 12 topic areas.

Our findings show that AI-assistance can effectively improve contouring accuracy and reduce intra- and interobserver variation and contouring time, which could have a positive impact on tumor control and patient survival,” wrote authors of a recent study published in Radiology.