Clinicians might be saved time and improve their diagnostic accuracy when deep learning is applied to echocardiography, according to a recent article from Cardiovascular Business (CVB) regarding this year's American College of Cardiology annual meeting in Orlando.

Photo courtesy of the University of Washington.

Diagnostic error remains a major setback to improving the quality of radiology and overall interpretation of imaging examinations. What consistently induces diagnostic error is a radiologist's cognitive bias, according to a recent article published in the American Journal of Roentgenology.

New research, published online March 13 in the Journal of the American Medical Association, shows that the U.S. spends twice as much on healthcare as any other high-income country in the world. Heavy utilization of imaging technology was a contributing factor.

A worldwide study, published in Current Problems in Diagnostic Radiology, revealed wide inconsistencies in reporting brain gadolinium deposition (GD) based on a lack of understanding and an effort to minimize anxiety in patients.

Once considered some of the most contractually stable and fiscally secure practitioners in all of U.S. medicine, radiologists are today concerned about their very future—and more than a few are right to be worried. From nosediving reimbursement to successive consolidation, from constantly expanding technologies to fitfully pinballing policymaking, the pressures have been varied and unrelenting for years now. What’s more, the pace of change is even now only accelerating. How best to rise to this moment with realistic hopes of emerging stronger than ever?

Radiologist Marc Liebeskind, MD, doesn’t need to strain his memory to recall a time when he saw a health insurer’s authorization process come between sound clinical judgment and a patient’s clearly indicated care.

Emergency departments (EDs) are busier than ever, and ED imaging volumes are up almost across the board.