When imaging brain tumors such as gliomas, machine learning may advance the use of imaging and augment clinical care for patients, according to a review published Oct. 17 in the American Journal of Roentgenology—specifically in tumor segmentation and MRI radiomics.

Health technology company Royal Philips has launched its global incubator program for startup companies looking to apply artificial intelligence (AI)-based solutions into healthcare.

Researchers and scientists with the Montefiore Medical Center in Bronx, New York, are developing an artificial intelligence (AI) tool to identify patients with a deadly lung condition.

The color scale used to optimize image interpretation has a measurable diagnostic effect on the interpretation of computed tomography perfusion (CTP) exams and apparent diffusion coefficient (ADC) images, according to an Oct. 15 study published in Clinical Radiology.

MRIguidance, a spin-off company of the University Medical Center Utrecht in the Netherlands, is developing an advanced MRI software that can characterize both soft tissue and bone without the use of radiation, according to a company news release published Oct. 16.

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.

“For those who are unfamiliar with the field of machine learning (ML), the emerging research can be daunting, with a wide variation in the terms used and the metrics presented,” wrote Guy S. Handelman, with Belfast City Hospital in Northern Ireland, U.K., in a recent AJR perspective.

Artificial intelligence could be key to driving down emergency room visits over the next five years, according to one research company's crystal ball.