Missed appointments continue to be a significant issue in radiology. In fact, according to one estimate, they can easily cost a mid-sized outpatient imaging center nearly $2 million annually.

AI models can interpret medical images with a diagnostic accuracy comparable to that of actual physicians, according to new findings published in The Lancet Digital Health. 

The Medical Imaging & Technology Alliance (MITA), American College of Radiology and other industry groups sent a letter to congressional leadership Tuesday, Sept. 24, asking for a permanent repeal of the “disastrous” medical device tax. A suspension of the tax is set to expire Jan. 1, 2020.

Every patient’s medical history should include a “Past Imaging History” section specifically designed to provide context about their health, according to a new commentary published in Current Problems in Diagnostic Radiology.

Researchers have developed an AI algorithm that can help identify patients who have suffered a stroke and would benefit from an endovascular thrombectomy.

Siemens Healthineers and HealthyWomen, a nonprofit focused on women’s health, have published the results of a new survey focused on patient comfort during mammograms.

Clinicians and patients can both be “nudged” in certain ways that improve decision-making related to imaging-based cancer screening, according to a new analysis published in the Journal of the American College of Radiology.

The results are in! The American College of Radiology (ACR) and Society for Imaging Informatics in Medicine (SIIM) announced the winners of the groups’ machine learning challenge during SIIM’s Conference on Machine Learning in Medical Imaging in Austin, Texas.

Screening ultrasound (US) examinations after digital breast tomosynthesis (DBT) and after digital mammography (DM) result in comparable cancer detection rates, according to a new study published in the American Journal of Roentgenology.