Enterprise Imaging

Enterprise imaging brings together all imaging exams, patient data and reports from across a healthcare system into one location to aid efficiency and economy of scale for data storage. This enables immediate access to images and reports any clinical user of the electronic medical record (EMR) across a healthcare system, regardless of location. Enterprise imaging (EI) systems replace the former system of using a variety of disparate, siloed picture archiving and communication systems (PACS), radiology information systems (RIS), and a variety of separate, dedicated workstations and logins to view or post-process different imaging modalities. Often these siloed systems cannot interoperate and cannot easily be connected. Web-based EI systems are becoming the standard across most healthcare systems to incorporate not only radiology, but also cardiology (CVIS), pathology and dozens of other departments to centralize all patient data into one cloud-based data storage and data management system.

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Charles E. Kahn, Jr., MD, MS, editor of the the RSNA journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine. He discusses the need to validate artificial intelligence (AI) algorithms with your own patient population to determine if it is accurate for a specific institutions patients. He also explains how bias can be inadvertently added into a algorithm, and how the AI may take learning shortcuts. #AI

VIDEO: Assessing radiology AI and understanding programatic bias 

Charles E. Kahn, Jr., MD, MS, editor of the the RSNA  journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, discusses the need to validate AI algorithms with your own patient population data.  

Specialty-specific workflow training could increase radiologist satisfaction with EHRs

Clinicians who receive specialty-specific workflow training are 24 times more likely to agree that the EHR meets their functionality needs, according to a new KLAS report.

Monique Rasband from KLAS Research shares trends in PACS and radiology informatics.

VIDEO: 6 key trends in PACS and radiology informatics observed by KLAS

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, shares some of technology trends observed in radiology PACS and and imaging informatics since 2019.

Validation and testing of all artificial intelligence (AI) algorithms is needed to eliminate any biases in the data used to train the AI, according to HIMSS.

VIDEO: Understanding biases in healthcare AI

Validation and testing of all algorithms is needed to eliminate any biases in the data used to train the AI, according to Julius Bogdan, vice president and general manager of the HIMSS Digital Health Advisory Team for North America.

Female Medical Research Scientist Working with Brain Scans

Chan Zuckerberg Initiative to help fund repeat imaging of 60,000 patients

The ambitious project seeks to exponentially increase new data within the U.K. Biobank, a prominent large-scale biomedical database and research resource. 

Intelerad acquires Life Image, becomes 'largest medical image exchange network in the world'

The acquisition represents a significant advance in interoperability and will combine networks that total over 80 billion images globally, including data from institutions such as Mayo Clinic, Cleveland Clinic and New York Presbyterian Hospital.

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Might AI automation improve peer review?

With the software’s help, the ratio of CTs requiring radiologist review to missed findings identified was 10:1, experts shared, adding that without the help of AI that ratio would be at least 66:1. 

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Deep learning reconstruction levels playing field between 1.5T and 3T MRI exams

Denoising using deep learning techniques can boost the performance of 1.5T MR brain imaging, resulting in quality comparable or superior to 3T imaging. 

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