Two short years after RadiologyBusiness.com added AI as a standalone beat, it seems the technology has burrowed into radiology like the Burmese python took to the Everglades. At first its presence was novel. Soon it became not uncommon. And now the infiltrator is in everyone’s head. It may as well be everywhere.

Just as changing circumstances impact how marriage partners work together for the sake of their families, new wrinkles in familiar processes affect the way radiologists and referring physicians cooperate for the good of their patients. And the “changing circumstances” and “new wrinkles” now include the strain of U.S. healthcare’s push for value-based care.

Given radiology’s development into a subspecialist-rewarding profession, is there pressure on radiology residents to choose a subspecialty or two and, in turn, aim to make generalized practice a side job? Should even established rads concentrate on one area as a way to remain relevant?

The Amerian Heart Association and the Duke Clinical Research Institute have formed a strategic alliance devoted to designing and conducting pragmatic randomized clinical trials in the cardiovascular space.

Convolutional neural networks (CNNs) can be trained to detect lung nodules on chest x-rays, according to a new study published in Artificial Intelligence in Medicine.

The SOMATOM X.cite premium single-source computed tomography scanner guides radiologists through each step of the exam process. 

Congress is seeking more information from a proposed rule from HHS that would allow third-party vendors to have access to patient data in order for consumers to view and access their own personal health information.

The models encompass a wide variety of diagnostic tasks, including pneumothorax detection on chest x-rays and highlighting brain segments on MRI scans.