News | Radiation Oncology | April 01, 2020
April 1, 2020 — ViewRay, Inc. announced a strategic collaboration with VieCure, an artificial intelligence (AI)...
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — Lung infections generated by the coronavirus can be detected in computed tomography (CT) images....

Doctor in our hospital is using this intelligent system for accurate diagnosis. (Photo: Business Wire)
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — The Intelligent Evaluation System of chest computed tomography (CT) for COVID-19, developed by YITU...
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — Two British companies at the leading edge of medical imaging technology are working together on a plan...
News | Artificial Intelligence | March 31, 2020
March 31, 2020 — An artificial intelligence tool accurately predicted which patients newly infected with the COVID-19...

A physician working in a coronavirus care center nearby Daegu, South Korea, is using Lunit INSIGHT CXR to interpret chest X-ray image of a coronavirus patient. Photo by Seoul National University Hospital
News | Artificial Intelligence | March 30, 2020
March 30, 2020 — Lunit, a medical AI software company that develops AI-powered analysis of lung diseases via chest X-...

Getty Images
Feature | November 16, 2020
In radiology departments, leveraging data and actionable knowledge are essential for making strategic clinical,...
News | Coronavirus (COVID-19) | March 27, 2020
March 27, 2020 — PaxeraHealth has spent years building and bettering its solutions and has been poised to respond to...

AI vendor Infervision's InferRead CT Pneumonia software uses artificial intelligence-assisted diagnosis to improve the overall efficiency of the radiology department. It is being developed in China as a high sensitivity detection aid for novel coronavirus pneumonia (COVID-19).
Feature | Coronavirus (COVID-19) | March 27, 2020 | Jilian Liu, M.D., HIMSS Greater China
An older couple walked into the Hubei Provincial Hospital of Integrated Chinese and Western Medicine near their...
Videos | Coronavirus (COVID-19) | March 25, 2020
Jilan Liu, M.D., MHA, CEO for the HIMSS Greater China, explains how health information technology (HIT) was leveraged...
News | Artificial Intelligence | March 24, 2020
March 24, 2020 — Qure.ai, a leading healthcare AI startup, announced it has developed additional capabilities for their...
News | Artificial Intelligence | March 23, 2020
March 23, 2020 — Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ...
News | Artificial Intelligence | March 23, 2020
March 23, 2020 — behold.ai announced that its artificial intelligence-based red dot algorithm quickly identifies chest...

Representative examples of the attention heatmaps generated using Grad-CAM method for (a) COVID-19, (b) CAP, and (c) Non-Pneumonia. The heatmaps are standard Jet colormap and overlapped on the original image, the red color highlights the activation region associated with the predicted class. COVID-19 = coronavirus disease 2019, CAP = community acquired pneumonia. Image courtesy of the journal Radiology
News | Coronavirus (COVID-19) | March 20, 2020
March 20, 2020 — An artificial intelligence deep learning model can accurately detect COVID-19 and differentiate it...
News | Artificial Intelligence | March 18, 2020
March 18, 2020 — RSIP Vision, a global leader in artificial intelligence (AI) and computer vision technology, announced...

Researchers who participated in the DM (digital mammography) DREAM Challenge.
News | Mammography | March 07, 2020
March 7, 2020 — The study is based on the results obtained in the Digital Mammography (DM) DREAM Challenge, an...

Figure 1: Depiction of the fully automated CT biomarkers tools used in this study. (A) Schematic depiction of the automated process for assessing fat, muscle, liver, aortic calcification, and bone from original abdominal CT scan data. (B) Case example in an asymptomatic 52-year-old man undergoing CT for colorectal cancer screening. At the time of CT screening, he had a body-mass index of 27·3 and Framingham risk score of 5% (low risk). However, several CT-based metabolic markers were indicative of underlying disease. Multivariate Cox model prediction based on these three CT-based results put the risk of cardiovascular event at 19% within 2 years, at 40% within 5 years, and at 67% within 10 years, and the risk of death at 4% within 2 years, 11% within 5 years, and 27% within 10 years. At longitudinal clinical follow-up, the patient suffered an acute myocardial infarction 3 years after this initial CT and died 12 years after CT at the age of 64 years. (C) Contrast-enhanced CT performed 7 months before death for minor trauma was interpreted as negative but does show significant progression of vascular calcification, visceral fat, and hepatic steatosis. HU=Hounsfield units.
News | Computed Tomography (CT) | March 06, 2020
March 6, 2020 — Researchers at the National Institutes of Health and the University of Wisconsin have demonstrated that...
News | Artificial Intelligence | March 05, 2020
March 5, 2020 — Caption Health, a medical AI company, announced that its flagship product, Caption AI, the first AI-...
News | Artificial Intelligence | March 05, 2020
March 5, 2020 — CLEW announced that it will be demonstrating the industry’s first-ever AI-powered critical care...

TeraRecon's End-to-End AI Ecosystem
News | Artificial Intelligence | March 04, 2020
March 4, 2020 — SymphonyAI Group, an operating group of leading business-to-business AI companies, today announced the...

Image courtesy of Pixabay
Sponsored Content | Blog | Artificial Intelligence | March 02, 2020
Artificial intelligence may be important not only to get image sharing technologies to work together – but to make...