Radiology researchers are turning to deep learning (DL) technology to make NLP even more effective—and it’s a growing trend that shows no signs of slowing down.  

Radiology researchers have been spent countless hours seeking a dose-reduction technique that provides high-quality images while still limiting the patient’s exposure to ionizing radiation as much as possible.

Stroke care is one of the fields where AI research has made its biggest impact in recent years, and a new study published in Radiology focused on yet another way this evolving technology can make a difference.

AI is making a monumental impact on the way radiologists and other imaging specialists deliver care, but some providers still can’t afford to make the necessary investments at this time.  

When research teams are developing deep learning models, they have to make certain decisions about the image resolutions used in their work. For instance, should they always aim to use the largest images possible? Or are there times when smaller images can get the job done?  

Researchers have developed a new quantitative framework that evaluates thyroid nodules at a level comparable to two expert radiologists.

Deep learning-based reconstruction (DLR) can reduce the radiation dose associated with low-dose chest and abdominal CT scans without sacrificing image quality, according to a new study published in the American Journal of Roentgenology.

The American Society of Radiologic Technologists (ASRT) has published a new white paper on AI and its potential impact on the work of medical imaging and radiation therapy professionals.