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“The work [with Synaptiq] is unprecedented in its scale and potential impact,” Mortenson Center’s Managing Director Laura MacDonald MacDonald said. “It ties together our center’s strengths in impact evaluation and sensor deployment to generate evidence that informs development tools, policy, and practice.” 
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    ⇲ Implement & Scale
    A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery. 
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      Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up. 
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        Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring. 
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          This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions. 
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            Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. 

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                  5 min read

                  How Artificial Intelligence is Revolutionizing Radiology

                  Featured Image

                  The number of life science papers describing artificial intelligence (AI) rose from 596 in 2010 to 12,422 in 2019 —an increase of more than 2,084 percent in only ten years. According to one peer-reviewed article published in Nature, “AI shows potential for diagnosing, managing and treating a wide variety of medical conditions.”

                  Nowhere is this potential more apparent than in radiology.

                  Over the last decade, peer-reviewed publications on AI for medical imaging have increased from approximately 100-150 per year to 700-800 per year. Magnetic resonance imaging and computed tomography account for about half of these publications; neuroradiology, roughly a third. However, experts predict that AI will soon revolutionize every type of radiology, with applications for image acquisition, processing, interpretation, reporting, and more. [2] 

                  In 2020, the United States Food and Drug Administration approved 100 AI-enabled medical devices. In 2021, it published a 350-item list of approved AI medical devices, 70 percent for medical imaging. [3] 

                  The Key Differentiators Driving Growth

                  Radiology’s natural abundance of visual data makes it well-suited to the early application of data-driven technologies like AI. Moreover, AI offers convenient solutions to two of the industry’s top challenges:

                  #1 - Precision

                  Reading medical images can be like searching for a need in a haystack. Nodules, lesions, and other health indicators targeted by medical imaging can be small and subtle—sometimes even invisible to the human eye.

                  AI can help radiologists read medical images by augmenting their human expertise with the accuracy of machine vision and the power of computer processing. A study published in Clinical Radiology found that AI helped radiologists reduce the rate of missed lung cancers in chest X-rays by 60 percent.[4] Another found that AI could help radiologists identify microscopic fractures invisible to the human eye with a 90 percent success rate.[5]

                  #2 - Workload

                  Experts estimate a shortage of 9.9 million healthcare professionals by 2030. This labor crisis is already in effect; one study found that the mean on-call workload for radiologists more than quadrupled between 2006 and 2020.

                  AI can help radiologists do more work in less time, reducing their workload (and burnout) by automating tasks and, where automation is impossible, increasing efficiency. A paper published in Pediatric Radiology suggests that “computer-aided detection (CAD) can decrease reading time by making the diagnostic process easier” and “[AI-enabled] image enhancement could not only shorten image acquisition time but also ease detection.”

                  Looking to the Future

                  Publication, approval, and adoption rates have risen dramatically over the last decade, to the benefit of patients and practitioners. Moreover, experts forecast that this growth will not slow. AI will continue to increase diagnostic accuracy and precision, reduce workload, and otherwise optimize radiologists’ work across specialties.


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                  About Synaptiq

                  Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation. 

                  Contact us if you have a problem to solve, a process to refine, or a question to ask.

                  You can learn more about our story through our past projects, blog, or podcast

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