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Technology Management


As the influence of social media platforms such as Twitter and Facebook grows, healthcare specialists have started adopting specific hashtags on those platforms to reach the largest number of users possible. In interventional radiology, for instance, the preferred hashtag is #IRad. Researchers tracked the evolution of that hashtag on Twitter, publishing their results in the Journal of Vascular and Interventional Radiology.

During a 2016 simulation exercise, researchers evaluated the ability of 32 different deep learning algorithms to detect lymph node metastases in patients with breast cancer. Each algorithm’s performance was then compared to that of a panel of 11 pathologists with time constraint (WTC). Overall, the team found that seven of the algorithms outperformed the panel of pathologists, publishing an in-depth analysis in JAMA.

At RSNA 2017 in Chicago, artificial intelligence (AI) and deep learning technologies were everywhere. Attendees rushed to learn as much as possible about AI, countless educational sessions touched on the topic and exhibitors made sure to mention it in their booths as much as possible. I wouldn’t quite say AI took over the show like some have suggested, but it did make quite an impression on everyone walking through the doors of McCormick Place.

The buzz around social media in radiology has skyrocketed in recent years, with more and more departments, private practices and specialists starting to use using the various platforms to their advantage. Of course, it’s about more than just using sites such as Facebook, Twitter and Instagram; to get the most out of these resources, one must also learn the differences between them.

Interest in artificial intelligence (AI) and machine learning at RSNA 2017 seems like it’s unprecedented—but the increased attention is quantifiable. More than 100 sessions delve into the topic at this year’s show in Chicago. Two years ago, less than 10 touched on such concepts.


Recent Headlines

What do Twitter users have to say about lung cancer?

Social media platforms have quickly become dominant outlets to discuss healthcare, including lung cancer-specific topics across the cancer prevention and control continuum.

Interoperability in Radiology: A Game of Inches

The health IT holy grail of nationwide interoperability remains top of mind in theory yet miles away in practice. The daunting distance of the road ahead was thrown into sharp relief in early October, when Health Affairs published American Hospital Association (AHA) survey data from 2015 showing that two of three U.S. hospitals can’t locate, retrieve, send and/or meaningfully integrate the electronic medical records (EMRs) of patients who received care at other provider sites (Health Aff (Millwood). 2017 Oct 1;36(10):1820-1827). 

Examining AI’s Impact on Breast Imaging

By Working Closely with AI Technologies, Radiologists Are Making Considerable Strides in Breast Cancer Treatment

3 common complaints about using social media and how to overcome them

As social media continues to grow in popularity, radiologists and radiology practices alike are using platforms such as Facebook, Twitter and Instagram to provide additional value to patients. A recent analysis published in the Journal of the American College of Radiology explored some examples of how users can get the most out of these new technological tools, including a look at some of the most common complaints and problems associated with social media.

How deep learning may revolutionize image-based diagnosis

For years, medical researchers and vendors have tried their hand at involving computer aided diagnosis (CAD) into patient care. However, slow integration has allowed for newer cognitive technologies such as deep neural networks, or deep learning technology, to find an onramp into radiology imaging.  

No, Health Imaging Is Not Trailing Behind Health IT

The computerization of healthcare continues to speed forward, and it’s not exactly flying below the radar. From mHealth to health-specific AI, from patient portals to portable patient data—to any of half a dozen other areas of techno-advancement currently generating buzz—it sometimes seems as though anything and everything having to do with HIT is a hit.

Artificial Intelligence in Radiology: The Game-Changer on Everyone’s Mind

AI’s Impact Will Be Monumental—Will Radiologists Go Along for the Ride or Be Left in the Dust?

New AMA initiative ties together health, tech sectors

The amount of healthcare data that currently exists is extensive—to say the least. The need for a single platform to house this vast amount of data and is vital. A new initiative from the American Medical Association (AMA) invites representatives from health and technology sectors to collaborate in solving this problem and attribute to a new era of patient care.  

Radiologists leverage AI to characterize cancerous breast lesions

Artificial Intelligence (AI) can now determine whether high-risk breast lesions (HRLs) are likely to become cancerous, in turn avoiding unnecessary surgery in nearly one-third of patients, according to a new study by researchers from Massachusetts General Hospital and Harvard Medical School in Boston. 

CAD use for digital screening mammography remains stable

In the last 10 years, the Breast Cancer Surveillance Consortium (BCSC) has released numerous studies that show computer-aided detection (CAD) for screening mammography can lead to decreased radiologist reading accuracy. According to a recent study published by the Journal of the American College of Radiology, however, CAD use at digital screening mammography facilities remained stable from 2008 to 2016.