That’s according to a new survey of healthcare stakeholders, highlighted in November’s European Journal of Radiology

A deep learning software tool powered by artificial intelligence has been proven to boost clinicians’ ability to detect lung cancer on chest x-rays. 

The investor-owned, Los Angeles-based provider announced Thursday, Nov. 7, that it’s teaming with to further spread AI tools across its 340 outpatient centers. 

Presagen announced Oct. 30 the launch of its AI Open Projects platform, a tool that allows radiology practices worldwide to share images and help to build AI products that are “robust, scalable and unbiased.” 

Case Western Reserve scientists have developed a tool that may help predict whether precancerous breast lumps will worsen, heading off the need for unnecessary radiation treatment. 

Researchers found that deep convolutional neural networks (CNNs) can predict sequence types for brain MR images, sharing their findings in the Journal of Digital Imaging.

Mammography does a good job detecting calcifications, but its specificity for distinguishing benign from malignant findings remains low.

A deep-learning algorithm can be as effective—or more effective—than radiologists in finding intercranial hemorrhages on CT scans.

Canon Medical Systems USA announced Monday, Oct. 21, that the Advanced Intelligent Clear-IQ Engine (AiCE) for its ultra-high resolution CT scanner has gained FDA approval.

Researchers trained the machine by feeding it almost 230,000 digital mammography exams and more than 1 million images.

Researchers are urging radiological professionals to stay visible and active as their institutions incorporate artificial intelligence into their imaging practices. 

“The impact of this groundbreaking solution for patients and healthcare providers is substantial," said President Brian Fleming.