Case Studies

A compilation of the latest news in AI and machine learning

As an integrated health-delivery network comprising 13 hospital campuses, two research centers and a health plan with more than half a million subscribers sitting atop the biggest biobank with whole exome (DNA) sequence data in existence, Pennsylvania’s Geisinger Health System is one of the best-positioned institutions in the U.S. to explore the possibilities and initial successes of AI in healthcare. The institution is bringing complex algorithmic concepts to everyday patient care and showing others the path forward.

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?

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.

Fourteen years ago, radiologist Dean R. Ball, DO, founded a breast imaging practice to meet the needs of the underserved communities in and surrounding Youngstown, Ohio. Today, the practice Ball founded, Tiffany Breast Care Center, employs 16 mammography staffers, up from five in 2004. Although the practice has grown significantly, Ball is committed to reading the X-ray images for each of his patients—which is upwards of 15,000 annually.