Artificial intelligence (AI) can help predict how a breast tumor will respond to neoadjuvant chemotherapy (NAC), according to new findings published in the Journal of Digital Imaging.

On Oct. 26, the American College of Radiology Data Science Institute (ACR DSI) announced the release of standardized artificial intelligence (AI) use cases designed to improve AI adoption in radiology. Why, exactly, are these use cases so vital to the specialty?

The American College of Radiology (ACR) Data Science Institute (DSI) announced the release of its first series of freely available standardized artificial intelligence (AI) use cases to increase the utilization of AI adoption in medical imaging.

Specialists can improve aneurysm detection rates by using a deep learning algorithm that provides a second assessment of images already interpreted by radiologists, according to new findings published in Radiology.

Machine learning (ML) has become one of the hottest topics in radiology and all of healthcare, but reading the latest and greatest ML research can be difficult, even for experienced medical professionals. A new analysis published in the American Journal of Roentgenology was written with that very problem in mind.

The Massachusetts Institute of Technology (MIT) announced a $1 billion commitment to “address the global opportunities and challenges presented by the prevalence of computing and the rise of artificial intelligence (AI).”

Researchers from Massachusetts General Hospital (MGH) and Harvard Medical School developed a deep learning (DL) model that measures breast density “at the level of an experienced mammographer.” Results of the study were published in Radiology.

William Hsu, PhD, an associate professor of radiological sciences at UCLA, is the first deputy editor of RSNA’s new online journal, Radiology: Artificial Intelligence.