Experts found that compared to the average effective doses of organs, breast doses were higher, representing increased risks of breast cancer for women. 

After experts from one institution evaluated 500 portable chest x-rays completed during the summer of 2021, it was revealed that 46.2% of the images obtained were problematic, requiring the imaging to be repeated. 

With the software’s help, the ratio of CTs requiring radiologist review to missed findings identified was 10:1, experts shared, adding that without the help of AI that ratio would be at least 66:1. 

Denoising using deep learning techniques can boost the performance of 1.5T MR brain imaging, resulting in quality comparable or superior to 3T imaging. 

One radiologist is being held liable for a patient’s injuries due to a “breach of standard of care” despite another radiologist also missing the same significant finding, Canadian Lawyer reports. 

To promote the legitimacy of their products, companies most often tout their partnerships with medical and academic institutions, in addition to their applications’ legal approvals.

As artificial intelligence (AI) adoption expands in radiology, there is growing concern that AI algorithms need to undergo quality assurance (QA) reviews.

Bibb Allen, MD, FACR, Chief Medical Officer of the American College of Radiology (ACR) Data Science Institute, and former ACR president, explains how hospitals or radiology departments can conduct quality assurance assessments on artificial intelligence algorithms they adopt to ensure they are accurate.