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.”

Scientists at the California Institute of Technology can now assess a person’s intelligence with nothing more than a brain scan and an AI algorithm, university officials announced this summer.

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?

Researchers have developed a fully integrated computer-aided diagnosis (CAD) system that detects, segments and classifies masses from mammograms using deep learning and a deep convolutional neural network (CNN), according to a new study published by the International Journal of Medical Informatics.

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.

Researchers have shown that machine learning can identify if a patient has schizophrenia by analyzing an MRI of their brain, according to a new study published in Molecular Psychiatry.

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.

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

An artificial intelligence (AI) system defeated a team of 15 doctors, 2-0, in two rounds of a competition that looked at the ability to diagnose brain tumors and predict the expansion of brain hematomas.

2018 has been an especially busy year for imaging leaders, with technology evolving at a rapid rate and healthcare policies continuing to change with the times. At AHRA 2018 in Orlando, many of those leaders will once again unite to share ideas, learn, network and have a little bit of fun. AHRA’s president, Angelic Bush, spoke with RBJ about some of the biggest trends in radiology right now and what she and her colleagues have planned for the big show in Orlando.