Case Studies

Artificial and augmented intelligence are driving the future of medical imaging. Tectonic is the only way to describe the trend. And medical imaging is at the right place at the right time. Imaging stands to get better, stronger, faster and more efficient thanks to artificial intelligence, including machine learning, deep learning, convolutional neural networks and natural language processing. So why is medical imaging ripe for AI? Check out the opportunities and hear what experts have to say—and see what you should be doing now if you haven’t already started.

Not just for years but for decades, the department of radiology at the University of Wisconsin School of Medicine and Public Health in Madison has been leading the charge on creating innovative technology and translating imaging research into clinical practice.

Countless predictions have been made about artificial intelligence and machine learning changing imaging screening and diagnosis at the point of patient care—and clinical studies and experience are now proving it. Radiologists say the impact is real in improving diagnosis of cancers and quality of care, consistency among readers and reducing read times and unnecessary biopsies. One shining example targets the evaluation of breast ultrasound imaging.

Smart technologies are often touted as the answer to some of cardiology’s greatest challenges in patient care and practice. But where does hyperbole end and reality begin with artificial intelligence, machine learning and deep learning?

Developments in vastly scalable IT infrastructure will soon increase the rate at which machine learning systems gain the capacity to transform the field of medical imaging across clinical, operational and business domains. Moreover, if the pace seems to be picking up, that’s because data management on a massive scale has advanced exponentially over just the past several years. 

A new project is seeking to make MRI scans up to 10 times faster by capturing less data. NYU’s Center for Advanced Imaging Innovation and Research (CAI2R) is working with the Facebook Artificial Intelligence Research group to “train artificial neural networks to recognize the underlying structure of the images to fill in views omitted from the accelerated scan.”

Machine learning is 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 written by a team at Northern Ireland’s Belfast City Hospital and published in the American Journal of Roentgenology was written with that very problem in mind.

Medical imaging is in a big battle with big data. There’s too much data in too many locations, and most often they are not well managed. Data are clearly imaging’s most abundant yet most underutilized strategic asset. 

If you’ve seen one data center, you’ve seen them all. That’s what Charles Rivers believed, at least.

Like every American academic healthcare institution, SUNY Downstate Medical Center in Brooklyn, N.Y., is a beehive of activity in three overlapping yet distinct areas of focus—patient care, physician education and medical research. 

Bill Lacy, vice president of medical informatics at FUJIFILM Medical Systems U.S.A., spoke with Radiology Business about AI’s impact on radiologist workflow and what the company has planned for HIMSS19.

A family from Pennsylvania’s Plain People community, which consists primarily of Amish and Mennonite families, recently took their child to Cardiology Care for Children (CCC), a small yet regionally renowned practice in Lancaster.