Artificial Intelligence
Fujifilm Showcases New DR Detectors and AI Initiative in Chicago
Fujifilm Medical Systems U.S.A, Inc, a provider of diagnostic imaging and medical informatics solutions, unveiled two new digital radiography (DR) detectors – the CALNEO Dual (available only in Japan) and the FDR ES – at the 2018 Radiological Society of North America (RSNA) annual meeting, November 25-30 in Chicago, Ill., USA. The company also presented, REiLI, its global Medical Imaging and Informatics Artificial Intelligence (AI) technology initiative, as well as hosted an educational symposium focusing on the impact of AI on enterprise imaging now and into the future. More...26 Nov 2018
MDW Unveils First Radiology Blockchain Platform at RSNA 2018
Medical Diagnostic Web unveiled the first radiology blockchain platform designed to connect all players in the diagnostic digital imaging ecosystem at the 2018 Radiological Society of North America (RSNA) annual meeting, November 25-30 in Chicago, Ill., USA. More...26 Nov 2018
Subtle Medical Showcases Al for PET and MRI Scans at RSNA 2018
Subtle Medical showcased SubtlePET, its artificial intelligence (AI)-powered technology, at the 2018 Radiological Society of North America annual meeting (RSNA 2018), November 25-30, in Chicago, Ill. SubtlePET is the company’s first AI software product that allows hospitals and imaging centers to efficiently reduce noise and enhance positron emission tomography (PET) image quality. More...23 Nov 2018
Philips Launches New Platform to Enable Development of AI Assets
Royal Philips showcased its latest innovations powered by adaptive intelligence at the 2018 Radiological Society of North America Annual Meeting (RSNA), which took place November 25-30, in Chicago, Ill., USA. The company introduced IntelliSpace Discovery 3.0, a comprehensive, open platform to enable the development and deployment of Artificial Intelligence (AI) assets in radiology aimed at supporting radiologists in their clinical and translational research. More...23 Nov 2018
![Image: Saliency map of deep learning model Inception V3 on the classification of Alzheimer disease. (a) A representative saliency map with anatomic overlay in 77-year-old man. (b) Average saliency map over 10 percent of Alzheimer’s Disease Neuroimaging Initiative set. (c) Average saliency map over independent test set. The closer a pixel color is to the \"High\" end of the color bar in the image, the more influence it has on the prediction of Alzheimer disease (Photo courtesy of UCSF).](https://globetechcdn.com/mobile_hospimedica/images/stories/articles/article_images/2018-11-23/SDD-614.jpg "Image: Saliency map of deep learning model Inception V3 on the classification of Alzheimer disease. (a) A representative saliency map with anatomic overlay in 77-year-old man. (b) Average saliency map over 10 percent of Alzheimer’s Disease Neuroimaging Initiative set. (c) Average saliency map over independent test set. The closer a pixel color is to the \\"High\\" end of the color bar in the image, the more influence it has on the prediction of Alzheimer disease (Photo courtesy of UCSF)."){kind=small}
AI Predicts Alzheimer’s Disease Years before Diagnosis
A new study on the application of deep learning (DL) to detect changes in brain metabolism predictive of Alzheimer’s disease (AD) has found that artificial intelligence (AI) improves the ability of brain imaging to predict AD. More...23 Nov 2018
UK to Set Up New GBP 10 Million Centre for Medical Imaging and AI
The UK’s London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare will train sophisticated artificial intelligence (AI) algorithms from NHS medical images and patient data, to create tools that will help clinicians speed up and improve diagnosis and care across a number of patient pathways, including dementia, heart failure and cancer. More...23 Nov 2018
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The Artificial Intelligence channel of HospiMedica keeps the reader informed about the latest news in AI-based clinical decision making, Medical knowledge engineering, Intelligent medical information systems and additional related fields.