Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

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Embracing AI: Why Now Is the Time for Medical Imaging

Sponsored by Pure Storage

Artificial and augmented intelligence are driving the future of medical imaging. Tectonic is the only way to describe the trend. And medical imaging is at the right place at the right time. Imaging stands to get better, stronger, faster and more efficient thanks to artificial intelligence, including machine learning, deep learning, convolutional neural networks and natural language processing. So why is medical imaging ripe for AI? Check out the opportunities and hear what experts have to say—and see what you should be doing now if you haven’t already started.

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

Sponsored by Pure Storage

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

Sponsored by Pure Storage

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?

Sponsored by Pure Storage

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

Sponsored by Pure Storage

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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Microsoft announces AI-powered healthcare chatbot

Microsoft unveiled a new tool that allows healthcare organizations to create their own AI-powered chatbots and virtual assistants for various services.

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VR app provides operating experience for medical students

GIBLIB, an Los Angeles-based streaming media platform, has launched a virtual reality (VR) app that provides an immersive operating room (OR) experience to medical students and physicians.

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Global healthcare robotics market to reach $11.4B by 2025

The development and use of robotic systems in healthcare is expected to surge over the next several years thanks to continuous technological advances, according to a report by BIS Research. The market intelligence firm predicts the global healthcare robotics market will reach $11.4 billion by 2025.

Around the web

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The American College of Cardiology has shared its perspective on new CMS payment policies, highlighting revenue concerns while providing key details for cardiologists and other cardiology professionals. 

As debate simmers over how best to regulate AI, experts continue to offer guidance on where to start, how to proceed and what to emphasize. A new resource models its recommendations on what its authors call the “SETO Loop.”