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    Pathology Is Going Digital: Lessons from Early Adopter

    Five years ago, two key takeaways from a survey of their pathologists sent NorthShore University HealthSystem toward the front lines of a technological revolution: digital pathology.

    The team almost unanimously agreed that, first, it was time to consider AI as an aid to microscopic tissue analysis. And second, 73% wanted the flexibility to work remotely at another site or at home, at least sometimes, via telepathology.

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    The Path to Digital Pathology: 3 Obstacles, 3 Opportunities

    It was about 2000 when Yale pathologist John Sinard, MD, PhD, first heard the prediction. “In five years, we won’t be using microscopes,” a respected peer quipped. “We’ll be examining all our slides as digitized images on computer monitors.”

    Nearly a quarter-century later, Sinard reports: “I’m at my workstation, and my microscope is sitting right here next to me.”

  • Memorial Sloan Kettering Cancer Center

    15 years into digital pathology, Memorial Sloan Kettering offers questions to ask, data to learn from

    With more than 7 million digitized slides on hand, the pathology department at Memorial Sloan Kettering Cancer Center in New York City represents one of the largest repositories of whole slide images in the world. It’s no surprise the library is so large, as it’s been accruing new images since 2008. And with total case volumes exceeding one million slide reads per year, the inventory continues to grow at that scale.

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    3 Steps to Managing a Growing Radiology Practice

    Listen in to see how Wake Radiology UNC Rex, a leading imaging group in the 4th fastest growing metro area in the U.S., boosted their speed and infrastructure to take care of more patients. Radiologists are happier, IT is too—and so was the board that saved money. Learn how they did it – and grab some tips for your next IT project. 

  • Managing imaging volume overload

    Managing Imaging Volume Overload and Controlling Cost

    Radiology Associates of Venice and Englewood (RaVE) needed a storage-re up to support huge growth in imaging exams and image volume per exam, a growing and graying patient base and their remote and onsite radiologists having access to all images instantly. Here’s how they did it—and had the solution installed in less than a week.

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NYU’s Daniel Sodickson on AI, Facebook and Why Faster MR Scans Could Improve Healthcare

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.”

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Machine Learning 101: Simplifying It One Term at a Time

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.

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[Expert Roundtable] Architecting AI: Rethinking Medical Imaging & Defining the Strategy

We asked the questions you want to: Why is imaging ripe for AI? How will improvements in image processing and reconstruction, quality control and work list prioritization improve the practice of radiology? 

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[Expert Roundtable] Architecting AI: Why Machine Learning Is Changing Medical Imaging

Learn how ML algorithms are helping radiologists to improve diagnosis, find more cancers, reduce biopsies and increase efficiency, and what IT departments need to know to deploy AI apps.

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Health IT Strategy Q&A: The Time Has Come to Extend Holistic Thinking from Patient Care to Data Storage

When Josh Gluck joined Pure Storage this past April, he arrived well-acquainted with the most pressing data-management issues affecting healthcare IT leaders today. 

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Q&A: In a Flash: How to Build an Enterprise Imaging AI Infrastructure

Building the infrastructure to support the accelerating adoption of AI in healthcare is the mission of Pure Storage and its FlashBlade technology, an all-flash scale-out object-based solution that can expand to petabytes of capacity. As Esteban Rubens says, infrastructure to power AI, machine learning and deep learning needs to be effortless, efficient and evergreen to ensure success today and into the future. Here’s how.

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Humans Keeping Machines in Check

Mark Michalski, MD, Executive Director of the MGH/BWH Center for Clinical Data Science gets to see, touch or hear about much of what’s happening in artificial intelligence.

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MDs: Get Ready to Change with AI

There are the believers in augmented medicine, with physicians and machines working hand in hand and improving care and patient outcomes. And there are the stalwarts who see machines taking over the tasks of mankind. Period.

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