Health Care Providers & Services


120 AI Predictions For 2020

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Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...


Mastering Intensive Care: Episode 49: Hugh Montgomery - "We've got to act right now"

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Climate change is a conversation we need to be having in Intensive Care circles. If the environmental catastrophe that is unfolding around us continues unabated there may no longer even be Intensive Care Units. The rising global temperatures, the melting ice, the extreme weather events, and their impact on agricultural crops and human habitation may well lead to such a fall in the economy that our healthcare system may not have the financial resources it does now. And given ICUs are the most expensive part of our hospitals, have a guess what might disappear first. So who is there better to listen to about the climate crisis than British intensivist, Professor Hugh Montgomery, a deeply passionate and highly intelligent man, who was a founding member of the UK Climate and Health Council, and who has helped raise awareness about climate change for over 2 decades.


120 AI Predictions For 2020

#artificialintelligence

Me: "Alexa, tell me what will happen in 2020." Amazon AI: "Here's what I found on Wikipedia: The 2020 UEFA European Football Championship…[continues to read from Wikipedia]" Me: "Alexa, give me a prediction for 2020." Amazon AI: "The universe has not revealed the answer to me." Well, some slight improvement over last year's responses, when Alexa's answer to the first question was "Do you want to open'this day in history'?" As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. "Vehicle AI is going to be ...


Philips extends AI portfolio with launch of IntelliSpace AI Workflow Suite to seamlessly integrate applications across imaging workflows

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Philips announces the launch of IntelliSpace AI Workflow Suite to enable healthcare providers to seamlessly integrate AI applications into the imaging workflow. Part of Philips' new enterprise imaging informatics solution, the comprehensive AI workflow platform provides a full suite of applications for integration and centralized workflow management of AI algorithms, delivering structured results wherever they're needed across the healthcare enterprise. Partners at launch include Aidoc, MaxQ AI, Quibim, Riverain Technologies and Zebra Medical. IntelliSpace AI Workflow Suite was unveiled at the 2019 Radiological Society of North America Annual Meeting (RSNA). Leiden University Medical Center (LUMC) in the Netherlands recently signed an agreement to be the first healthcare provider to install the platform.


Nines banks $16.5M for tele-radiology, AI imaging triage platform

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Nines, a tele-radiology and artificial intelligence startup, unveiled its software platform alongside news of a $16.5 million Series A raise. The funding was led by Accel and 8VC, with individual participants also taking part. The two-year-old startup's efforts are broken into two camps. On the one hand is a tele-radiology service that's staffed by live specialists, and according to the company is bolstered by partnerships with institutions such as the Mount Sinai Health System. Its other work involves the startup's investigational machine learning platform intended to support imaging data analysis.


Geisinger-AI vendor aim to reduce adverse events, avoid readmissions

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Israel-based Medial EarlySign and Geisinger Health System have partnered to apply advanced artificial intelligence and machine learning algorithms to Medicare claims data to predict and improve patient outcomes. An EarlySign-Geisinger proposal has been selected as one of 25 participants to advance to Stage 1 of a technology challenge from the Centers for Medicare and Medicaid Services to accelerate the development of AI and machine learning solutions for healthcare. "Approximately 4.3 million hospital readmissions occur each year in the U.S., costing more than $60 billion, with preventable adverse patient events creating additional clinical and financial burdens for both patients and healthcare systems," says David Vawdrey, Geisinger's chief data informatics officer. "Together with our partner EarlySign, we have forged a dynamic team that is rapidly developing novel solutions to achieve the Quadruple Aim of improving the patient experience of care, improving the health of populations, reducing cost and improving clinical care provider satisfaction," adds Vawdrey. The AI vendor and Danville, Penn.-based regional healthcare provider intend to develop models that predict unplanned hospital and skilled nursing facility admissions within 30 days of discharge and adverse events such as respiratory failure, postoperative pulmonary embolism or deep vein thrombosis, as well as postoperative sepsis before they occur.


AI Can Now Make Medical Predictions from Raw Data Through 'Deep Learning.' But Can it Be Trusted?

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Already, at Massachusetts General Hospital in Boston, "every one of the 50,000 screening mammograms we do every year is processed through our deep learning model, and that information is provided to the radiologist," says Constance Lehman, chief of the hospital's breast imaging division. In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable -- hidden inside a so-called black box -- how can it be trusted?


5 ways Data Intelligence is disrupting Healthcare -

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Data intelligence is driving the next wave of revolution in healthcare. As providers adopt a more holistic data-centric approach, it will lead to better treatment outcomes, personalized treatment, and preventive interventions. One of the disruptive trends to watch in recent times is the way data is being democratized in the healthcare industry. From a siloed approach to the use of technology and data, we are now witnessing data-driven value creation across the ecosystem. As new data technologies with advanced intelligence capabilities emerge healthcare companies now have an opportunity to better capitalize on data, innovate patient care and drive profitability while managing growing risks in patient privacy and data security.


AI Weekly: Amazon plays the long game in health care AI

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Today concludes Amazon's re:Invent 2019 conference in Las Vegas, where the Seattle company's Amazon Web Services (AWS) division unveiled enhancements heading down its public cloud pipeline. Just Tuesday, Amazon announced the general availability of AWS Outposts, a fully managed service that extends AWS' infrastructure and services to customer datacenters, co-location spaces, and on-premises facilities. And it debuted in preview Amazon Detective, which helps to analyze, investigate, and identify the root cause of potential security issues and suspicious activities. That's not to mention AI-powered fraud detection and code review products and an expanded machine learning experimentation and development studio, as well as a dedicated instance for AI inferencing workloads. But perhaps the most intriguing launch this week was that of Amazon Transcribe Medical, a service that's designed to transcribe medical speech for clinical staff in primary care settings.


3 questions to ask before investing in machine learning for pop health

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The goal of population health is to use data to identify those who will benefit from intervention sooner, typically in an effort to prevent unnecessary hospital admissions. Machine learning introduces the potential of moving population health away from one-size-fits-all risk scores and toward matching individuals to specific interventions. The combination of the two has enormous potential. However, many of the factors that will determine success or failure have nothing to do with technology and should be considered before investing in machine learning or population health. Population health software, with or without machine learning, only produces suggestions.