Artificial Intelligence

Artificial intelligence (AI) and machine learning often get lumped together, but as the authors of a new Radiology commentary explained, the two terms are far from interchangeable. While machine learning is a specific field of data science that gives computers the ability to “learn” without being programmed with specific rules, AI is a more comprehensive term used to describe computers performing intelligent functions such as problem solving, planning, language processing and, yes, “learning.”

San Francisco-based tech company Bay Labs this week announced the success of its deep learning software, EchoMD AutoEF, in reducing variability in cardiovascular imaging.

A machine learning tool developed by researchers at Imperial College London could assess the severity of leukoaraiosis in stroke patients with greater efficiency and accuracy than the typical emergency room CT, a study published this week in Radiology states.

As the relationship between radiology and artificial intelligence (AI) continues to evolve, radiology trainees may find themselves wondering what, exactly, they should know about these groundbreaking technologies. Do they need to be AI experts? Can they just avoid the subject altogether?

RSNA announced this week that it will be offering a new Spotlight Course focused on artificial intelligence (AI) September 23-24 at the Espace Saint-Martin in Paris.

Radiologists are “significantly influenced” by contextual bias when interpreting mammograms, according to a new study published in the Journal of Medical Imaging.

Researchers from the Massachusetts Institute of Technology (MIT) in Cambridge have been studying a machine learning algorithm they say makes the process of medical image registration more than 1,000 times faster.

A team of Hong Kong scientists led by Kwok Ka-wai, PhD, have developed the world’s first intraoperative MRI-guided robot for bilateral stereotactic neurosurgery, opening new doors for less invasive, safer and more accurate treatment of conditions like Parkinson’s disease.

Mercy Radiology, a New Zealand-based imaging provider, has plans to use artificial intelligence (AI) algorithms to help with the detection of prostate cancer.

A machine learning-based “red dot” triage system could help differentiate between normal and abnormal chest radiographs while optimizing clinician workflow, British researchers reported this month in Clinical Radiology.

The American Society of Neuroradiology (ASNR) announced that Peter Chang, MD, a neuroradiology fellow at the University of California San Francisco, has received the Cornelius G. Dyke Memorial Award for his recent research involving deep learning technologies.

Israeli medical imaging startup Zebra Medical Vision has raised $30 million series C venture capital funds to create artificial intelligence (AI)-based tools for radiologists. At present, Zebra has raised a total of $50 million in funds.