Many institutions are making radiology reports available via online portals, but can patients actually understand the information in them?

Bypassing the blood-brain barrier has long been a challenge for clinicians, but focused ultrasound can open specific pathways and help deliver targeted treatments to those suffering from the disease.

Hospital radiology departments hoping to survive and thrive in healthcare’s volume-to-value era must get creative—and even somewhat aggressive—about growing their outpatient business.

The technique combines AI with patient-specific health and cost information for a rough estimate on an individual's five-year healthcare expenditures.

The challenge tasked teams with developing an algorithm capable of identifying and classifying subtypes of hemorrhages on head CT scans.

Valerie P. Jackson, MD, president of RSNA, suggested that radiologists look within themselves, step out of the reading room and connect with others to move toward this “long desired goal."

While AI wasn’t the only topic discussed during the SIIM 2019 annual meeting, every issue seemed to be tied to the emerging technology in one way or another.

Data security has become a serious issue in the U.S., not only for big tech companies like Facebook, but for vendors and institutions looking to use patient imaging information to develop AI platforms.

In the first five months of the EHR, radiology information system (RIS) and PACS deployment project, Robert Paul, a radiology informatics manager at Mayo Clinic in Arizona, lost 60% of his team. He described his efforts to reduce burnout among his staff during a presentation at the SIIM annual conference.

A deep neural network platform can help radiologists detect abdominal aortic aneurysms (AAAs) on CT images, and is especially helpful in clinically challenging cases, according to research presented at the SIIM annual conference.

Blockchain could be used to streamline preauthorization, share images between institutions and empower patients. But if healthcare as a whole isn't interested in sharing data, no technology can solve the industry's imaging informatics problems.

 

A convolutional neural network (CNN) approach can accurately identify and sub-classify suspected tuberculosis (TB) on chest radiographs, according to research presented at the Society for Imaging Informatics in Medicine (SIIM) annual meeting.