Artificial Intelligence

A team at the University of Toronto has successfully developed artificial intelligence (AI) that helps automate the radiation therapy planning process, potentially saving radiologists from several days of work on just one patient.

The global market for artificial intelligence (AI) in medical imaging is expected to see significant growth in the years ahead, topping $2 billion by 2023, according to a new report from Signify Research.

The Radiological Society of North America (RSNA) is staying future-focused for its annual symposium in Chicago in November. According to a statement from the organization, machine learning and artificial intelligence (AI) will be playing an expanded role at this year’s conference.

RSNA announced Wednesday, August 1, that it has big plans for educating members about artificial intelligence (AI) and machine learning (ML) for 2018 and beyond.

Deep learning technology can be used to evaluate MR images of the knee, according to a new study published in Radiology.

Artificial intelligence (AI) is an immensely popular topic in radiology, sparking countless discussions and debates about whether it will give radiologists a new tool for providing high-quality patient care or end up replacing them altogether.

Sham Sokka, PhD, has spent the bulk of his career in radiology, where he’s worked for 15 years with a range of clients to shape and customize imaging modalities, workflows and software.

Researchers have shown that they can use artificial intelligence (AI) to restore low-quality photos by exposing a neural network to only other low-quality photos, according to work presented at the International Conference on Machine Learning in Stockholm.

As the influence of artificial intelligence (AI) continues to grow in radiology, the specialty must come together to re-examine its ethics and code of behavior, according to a new commentary published in the Journal of the American College of Radiology.

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.

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.

The American College of Radiology Data Science Institute (ACR DSI) announced this week that it is co-sponsoring a National Institute of Biomedical Imaging and Bioengineering (NIBIB) workshop about artificial intelligence (AI) in medical imaging.