Case Studies

Artificial intelligence (AI) has been one of the most hyped advancements in radiology for many years now, sparking vigorous debates about its potential impact on patient care. But early on, discussions about AI were more theoretical than factual; it was simply too early to know what the future may bring.

MEDNAX Radiology Education has begun offering radiologists weekly CME sessions that take around 15 minutes to complete and were more than a decade in the making.

For physicians who treat Medicare patients, everything changes next New Year’s Day. 

That’s when real consequences begin to befall those who order advanced imaging—CT, MRI or PET scans—without first consulting appropriate use criteria (AUC). They’ll need to show they did so by using a CMS-qualified clinical decision support mechanism (qCDSM).

Sometimes a move in the right direction starts with a good hard look in the rearview mirror, then breaking it off to start anew with greater wisdom.

Buying new software and systems for your healthcare enterprise can be a precarious endeavor. On the one hand, replacing an old system that is holding you back or purchasing new functionality that will increase efficiency is a promising and positive thing. On the other, selecting the wrong vendor could cause delays, setbacks and even security incidents.

It was only recently that 10 radiology practices from around the United States formed a new managed services organization (MSO), named it Unified Radiology and took aim at securing independence for each member practice. Almost immediately, members discovered that the modest investment of time creating the MSO would result in significant financial returns, now and in the future.

One of the biggest ongoing trends in healthcare in recent years has been the increased focus on educating women about breast density. Dense breast tissue can obscure small masses and lower the sensitivity of mammograms, making it especially vital that women know their options if mammography reveals they have dense breasts.

When it comes to digital medicine, digital pathology is very late to the game. But its time is coming. And the benefits could be many: Bolstering the capabilities, efficiency and reach of individual pathologists, cutting patient wait times, streamlining multidisciplinary team meetings (MDTs) and offering more data-rich decision-making. It could even obviate a shortage of pathologists. Where does it fit into your strategic plan?

It’s all about the data. We’ve been saying this for years. We can choose to look at this in one of two ways. It’s either a constant truism or it actually evolves and gains mass over time. In the age of artificial intelligence, it is both. 

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.