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Healthcare is in an intense era of retooling, similar to the Industrial Revolution of several centuries ago. Bright minds in healthcare and technology are shaping new tools powered and empowered by artificial intelligence, machine learning and deep learning. This burgeoning Age of Intelligence is matching minds and machines to sharpen knowledge and insight to improve the delivery of care for patients, populations, practitioners and providers. 
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The power of artificial intelligence (AI) is enabling clinical breakthroughs that identify biomarkers without invasive procedures, diagnose skin cancer with a photograph, predict adverse clinical events, and recommend treatments based on current literature. Getting these innovations to market requires access to large, complex data sets to train the AI models.
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Lawrence Tanenbaum is a big believer in AI, as a tool to create better images, offer a more comprehensive view of a patient and more effectively handle imaging’s increasing volume and complexity. Bigger yet, AI is the impetus to change the way radiology and medicine are practiced across the care spectrum.
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When it comes to AI and machine learning, the regulatory trail has been blazed and the approval gates through open. The FDA has approved a couple dozen apps over the last year and a half—and the momentum is clearly building with Scott Gottlieb at the agency’s helm and recent moves to ramp up staffing to meet the demand.  
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(Spoiler alert: It’s a 69-page report that indicates the use of AI in healthcare is both promising and doable.)
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AI is hotter than hot in healthcare, according to AI market watcher CB Insights. Healthcare-AI funding reached $2.14 billion across 323 deals from 2012 through the second quarter of 2017—and has consistently been the top industry for AI deals.
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To look into the future is to catch only a glimpse inside Simon Warfield’s radiology research lab at Boston Children’s Hospital. His team is pairing hyperfast imaging and deep learning to push the limits of medical imaging and artificial intelligence (AI) to identify, prevent and treat disease. He’s also eyeing ways AI will help as data sharing expands among research sites. “The research world needs to look forward to manage forward,” he says.
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Ever the visionary, Paul Chang sees AI as an asset to radiologists. As he sees it, “AI and deep learning doesn’t replace us. It frees us to do more valuable work.” The vice chair of radiology informatics at University of Chicago Medicine takes a quick look through the crystal ball at the four stand-out challenges facing radiology with the rise of AI.
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When it comes to teaching new dogs new tricks, radiology training programs need to be thinking about updating their curricula and preparing for both the short- and the long-term effects of AI and machine learning, according to “Toward Augmented Radiologists,” a new commentary published online in March in Academic Radiology.
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Population health is absolutely something we want to target. To do that, we are using our archive of images that includes radiology, cardiovascular, interoperative and dermatology. For example, we’re looking at body composition—the amount of muscle, visceral fat and superficial fat. And common sense makes sense. Body composition correlates with how well patients do. In some cases, abdominal fat can even be an early biomarker of some cancers, like pancreatic cancer.
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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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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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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.