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Forward-looking providers are converting reams of data from myriad sources into innovative new ways to deliver healthcare and improve efficiencies.

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As costs continue to rise, healthcare organizations must become more efficient with collecting, says Anthony Cunningham, MBA, vice president of Patient Financial Services at Wake Forest Baptist Health. One approach, he explains, is deploying staff away from repetitive tasks and “toward high-value-add work.” That’s where artificial intelligence comes in.

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In a world of networked medical devices, it’s not hard to imagine a radiology-heavy cyberattack that is not only malicious but also ingenious.
 

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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. 

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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. 

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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.

A compilation of the latest news in AI and machine learning

Around the web

A new scientific statement from the American Heart Association explores the many ways AI and machine learning are being used to improve care for heart patients.

The new collaboration is designed to ensure patients who may face an increased risk of heart disease receive the follow-up care they need.

The new algorithm from Viz.ai is capable of identifying, labeling and quantifying brain bleeds in noncontrast CT images.