According to a Feb. 13 press release, the FDA announced clearance for the marketing of the Viz.AI Contact application, a clinical decision support software created to analyze CT results and notify providers of a potential stroke.

According to a December 2017 research survey conducted by the healthcare market research firm Reaction Data, most hospitals and imaging centers will be using machine learning or artificial intelligence (AI) technology by 2020.

As industry experts continue to explore artificial intelligence (AI) applications in radiology, the question remains of whether AI applications can and will add value, including in new knowledge and information to provide patients with better outcomes at lower costs.

Artificial intelligence and machine learning are all the rage—and for good reason. But researchers claim the brain doesn’t actually use the regions identified by machine learning to perform a task. Rather, these algorithms reflect the mental associations related to the task.

As medical imaging continues to evolve, Health Imaging spoke with enterprise imaging and health informatics expert Paul Chang, MD, professor of radiology and vice chair rad informatics at the University of Chicago Medicine, about what practitioners and healthcare technology leaders should keep in mind regarding artificial intelligence (AI) and deep learning in the coming year.  

Myelin, a fatty substance that sheaths nerves, is often damaged or missing in individuals with multiple sclerosis (MS). Physicians have depended on MR to detect demyelination, but the modality is incapable of differentiating between lost or damaged myelin and inflammation.

A research team with Boston University of Medicine has demonstrated artificial intelligence (AI) can analyze kidney biopsy images more accurately than traditional human methods.

MRI image analysis and machine learning may be a more accurate and efficient when applied to fetal MRI findings to predict the need for postnatal cerebrospinal fluid (CSF) diversion, according to a retrospective analysis study recently published by JAMA. According the study, researchers were able to develop a prognostic information model that can guide a more efficient candidate selection for potential fetal surgery.

South Korean researchers have used a budding machine-learning technique to generate high-quality structural MR images from amyloid PET scans of dementia patients’ brains. They were then able to quantify cortical amyloid load from these MR-less images, which may open the door to ordering PET scans alone for numerous imaging scenarios in which PET/MR is now a preferred diagnostic pathway.