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.

The variables involved in minimizing radiation exposure during CT imaging are many—but simply placing a patient off-center can result in a surface dose increase of 12 to 49 percent and negatively impact image quality.

Radiologists are subject to a myriad of interruptions. These frequent disturbances increase reading times and reduce accuracy with potentially negative consequences for both patients and providers, according to a recent Academic Radiology study.

As email, text alerts and other modern forms of communication become the norm, radiology departments cite phone calls as a frequent source of interruptions in radiology reporting rooms. Recent research published in Current Problems in Diagnostic Radiology provided five recommendations to educate referrers on how to eliminate unnecessary phone calls to radiology departments.

A group of New England researchers analyzed patient narratives, finding clinical codes and communication breakdown as the most common reasons for complaints, with no significant difference between multifactorial and single-coded complaints.

According to an article published in the Journal of the American College of Radiology, the rise of PACS and digital reading rooms are contributing to work-related musculoskeletal injuries among radiologists.

Effective communication between medical imaging providers and pediatric patients and guardians about radiation risks remains problematic, despite improvements in imaging technology and protocol optimization, according to an article published in the Journal of the American College of Radiology.

A group of U.S. researchers created a natural language processing (NLP) system which outperformed traditional rule-based methods in identifying lumbar spine findings, according to a study published online in Academic Radiology.

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.

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.