Artificial Intelligence for Cars May Drive Future of Healthcare

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The same artificial intelligence that may soon drive your new car is being adapted to help drive interventional radiology care for patients. Researchers at the University of California, Los Angeles (UCLA), have used advanced artificial intelligence, also called machine learning, to create a "chatbot" or Virtual Interventional Radiologist (VIR). This device communicates automatically with a patient's physicians and can quickly offer evidence-based answers to frequently asked questions. The scientists will present their research today at the Society of Interventional Radiology's 2017 annual scientific meeting in Washington, D.C. This breakthrough will allow clinicians to give patients real-time information on interventional radiology procedures as well as planning the next step of their treatment.


UCLA uses artificial intelligence to create virtual radiology advisor

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Interventional radiologists at the UCLA Medical Center are leveraging artificial intelligence to create a "chatbot" that automatically communicates with referring clinicians, providing them with evidence-based answers to frequently asked questions. Currently, the AI-powered prototype is being tested by a small UCLA team of hospitalists, radiation oncologists and interventional radiologists. The machine learning application, which acts like a virtual radiology assistant, enables clinicians to rapidly access valuable information while enabling them to perform other duties and to focus on patient care. The information is delivered in multiple formats, including relevant websites, infographics, and subprograms within the application. And if the tool determines that an answer requires a human response, contact information for an actual interventional radiologist is provided.


Imaging Technology News

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Burnout has become a popular buzzword in today's business world, meant to describe prolonged periods of stress in the workplace leading to feelings of depression and dissatisfaction with one's occupation. The topic has become so pervasive that the World Health Organization (WHO) addressed it at its 2019 World Health Assembly in Geneva in May, adding burnout to the 11th revision of the International Classification of Diseases (ICD-11) -- although classifying it as an "occupational phenomenon" rather than a medical condition. Healthcare itself is not immune to burnout, and a recent study in Journal of the American College of Radiology demonstrates it is taking a toll on pediatric radiologists in particular. The study surveyed Society of Pediatric Radiology (SPR) members and found nearly two-thirds expressed at least one symptom of burnout. While burnout is a complicated phenomenon and no two people experience it the same way, a commentary on the study suggests artificial intelligence (AI) could help alleviate some of the difficulties that can lead to burnout.


How AI Lays the Groundwork for Tomorrow's Healthcare

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In a policy recommendation passed this year by the American Medical Association, the organization lauds the potential of artificial intelligence in healthcare. Combining AI with human clinicians can advance care delivery "in a way that outperforms what either can do alone," the AMA says. While such technology has been considerably hyped in recent years, the organization understands that with tempered expectations -- and deployed in the right situations -- AI can have a real impact on the industry. SIGN UP: Get more news from the HealthTech newsletter in your inbox every two weeks! Many believe one of AI's biggest impact areas will be radiology.


AI Will Change Radiology, but It Won't Replace Radiologists

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Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings. That said, deep learning is rapidly advancing, and it's a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology.