The model has shown early promise, producing a five-fold increase in radiologists’ reported rate of finding significant errors. 

Experts recently made their pitch for informatics as a burnout-buster in a new analysis, set to be published in February’s Clinical Imaging. 

Researchers with Beth Israel Deaconess Medical Center, in Boston, recently made that discovery through a years-long experiment involving tens of thousands of patients. 

With so many gunshot victims requiring some type of imaging, radiologists can play a “pivotal” role in addressing this epidemic of violence, including building a database to better track violence’s aftermath. 

Forty years after physicist Allan Cormack and electrical engineer Godfrey Hounsfield jointly won a Nobel Prize for inventing computed tomography as we know it, the modality continues to generate new or improved uses and iterations. RBJ spoke with several trailblazers who are still plumbing the depths of CT applications.

Those include the company’s cost-conscious Focus 35C Detector and its Dual-Energy imaging application, which uses two filter materials to automatically switch between high- and low-energy exposures. 

Typically, primary care doctors must manually check the EHR to figure out whether a patient is eligible for a screening, but that step often gets lost during the course of a busy day.

The Washington Post revealed the last-minute decision—which occurred in 2017, but was never reported—in a story published Friday, Nov. 15. 

Presagen announced Oct. 30 the launch of its AI Open Projects platform, a tool that allows radiology practices worldwide to share images and help to build AI products that are “robust, scalable and unbiased.” 

Those involved said that such access is crucial for early cancer detection, and will hopefully help to avoid any unnecessary, duplicative testing in the future. 

Imaging informaticists can make a big impact on AI strategies, according to a new analysis published in Academic Radiology.

Commercially available face recognition software accurately identified patient’s based solely on their brain MRI scans. The findings suggest more resources must be put into securing imaging data.