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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Lack of transparency in AI research limits reproducibility, renders work 'worthless'

A recent analysis found that a significant amount of studies do not provide information pertaining to their raw data, source code or model. As a result, up to 97% of these studies do not produce systems that are fit to be used in real-world clinical scenarios. 

December 19, 2022
An example of artificial intelligence (AI) automated detection of a intracranial hemorrhage (ICH) in. a CT scan used to send alerts to the stroke acute care team before a radiologist even sees the exam. Example shown by TeraRecon at RSNA 2022.

VIDEO: Radiology AI aids acute care and other departments

Sanjay Parekh, PhD, senior market analyst with Signify Research, explains how some radiology AI is being adopted outside of radiology departments to improve care.

December 15, 2022
radiology reporting EHR health record CDS AUC

Follow-up care improves with reporting template for incidental findings

Use of the template, which included PCP notifications, also resulted in an increase of biochemical testing, follow-up imaging and specialist referrals in patients with incidental adrenal masses.

December 14, 2022
Example of AI automated detection and highlighting of critical lung findings on a chest X-ray for a possible lung cancer nodule and fibrosis. Example shown by AI vendor Lunit.

VIDEO: Radiology AI trends at RSNA 2022

Sanjay Parekh, PhD, senior market analyst with Signify Research, discusses trends in radiology AI seen on the expo floor and in sessions at RSNA 2022.

December 12, 2022
Dynamic lung air flow analysis just using X-ray without any contrast with new technology from 4D Medical.

PHOTO GALLERY: New technology and trends at RSNA 2022

Images from the Radiological Society of North America (RSNA) 2022 annual meeting Nov. 27- Dec. 1 in Chicago. The gallery includes new technologies and a look at sights around the world's largest radiology conference. 

December 1, 2022
hospital emergency room

Sunshine and rainbows and trauma: How weather can impact CT volume in EDs

This week at the annual RSNA meeting, the worlds of radiologists and meteorologists collided when researchers presented evidence of associations between certain weather conditions and patients presenting with polytrauma. 

November 30, 2022
knee x-ray

How AI-generated 'fake' X-rays can further medical research

But how reliable can synthetic radiologic images be in research settings? Are they as good as the real thing? 

November 17, 2022

Konica Minolta Healthcare to Extend Exa Platform to the Cloud with AWS

Exa SaaS helps healthcare organizations be more flexible, agile and scalable in deploying and managing software.

November 15, 2022

Around the web

Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans. 

"Gen AI can help tackle repetitive tasks and provide insights into massive datasets, saving valuable time," Thomas Kurian, CEO of Google Cloud, said Tuesday. 

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