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Bullish on AI: The Wisconsin Way: Reengineering Imaging & Image Strategy

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.

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ML’s Role in Building Confidence and Value in Breast Imaging

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.

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Will ‘Smart’ Solutions Really Transform Cardiology?

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?

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Matching Machine Learning and Medical Imaging: Predictions for 2019

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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[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.