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.

The semi-automated, quantitative 3D approach—joint space mapping (JSM)—detects small changes in joints and is both accurate and precise in measuring joint space compared to traditional two-dimensional (2D) radiography.

It’s long past asking “if” artificial intelligence (AI) and related technologies will revolutionize healthcare. According to a recent survey, 80 percent of executives expect AI will be integrated into the patient experience within two years. At the same time, 81 percent of respondents agree their organizations are not ready for the societal and liability issues that will result from this change.

The American Medical Association (AMA) has passed a policy addressing "augmented intelligence"—and not "artificial intelligence"—that provides recommendations for stakeholders' concerns.

The University of Kansas Health System’s new Indian Creek Campus now houses two fluoroscopy three-dimensional acquisition machines, which leaders of the system say are only one of four locations in the world to possess such technology, according to a fox4kc.com report.

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.

Intrinsic susceptibility biomarkers were found to successfully provide cross validation of the oxygen-enhanced MRI biomarker perfused Oxy-R when mapping tumor hypoxia in renal carcinoma, according to research published in Radiology.