radiologists

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How Machine Learning May Revolutionize Medicine

Bob Tedeschi | STAT | October 3, 2016

Doctors will one day be able to more accurately predict how long patients with fatal diseases will live. Medical systems will learn how to save money by skipping expensive and unnecessary tests. Radiologists will be replaced by computer algorithms. These are just some of the realities patients and doctors should prepare for as “machine learning” enters the world of medicine, according to Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, and Dr. Ezekiel Emanuel of the University of Pennsylvania, who recently coauthored an article in the New England Journal of Medicine on the topic...

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Interstitial Lung Disease (ILD) Experts And Advocates Announce Formation Of Open Source Imaging Consortium (OSIC)

Press Release | Open Source Imaging Consortium (OSIC) | May 22, 2019

An international group of leading experts and advocates in the fight against idiopathic pulmonary fibrosis (IPF), fibrosing interstitial lung diseases (ILDs), and other respiratory diseases including emphysematous conditions announced today the formation of the Open Source Imaging Consortium (OSIC). This global, not-for-profit organization is a cooperative and open source effort between academia, industry and philanthropy to enable rapid advances in the detection and diagnosis of these conditions through digital imaging and machine learning.

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RSNA Building an Open Repository of COVID-19 Imaging Data

Press Release | Radiological Society of North America (RSNA) | March 30, 2020

The medical imaging community around the world is uniting to help address the COVID-19 pandemic. The Radiological Society of North America (RSNA) continues to build on its extensive body of COVID-19 research and education resources, announcing a new initiative to build a COVID-19 Imaging Data Repository. The open data repository will compile images and correlative data from institutions, practices and societies around the world to create a comprehensive source for COVID-19 research and education efforts. The image hosting, annotation and analysis framework will enable researchers to understand epidemiological trends and to generate new AI algorithms to assist with COVID-19 disease detection, differentiation from other pneumonias and quantification of lung involvement on CT for prognosis or therapy planning.

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