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.

Example of artificial intelligence generated measurements to quantify the size of a lung cancer nodule during a followup CT scan to see if the lesion is regressing with treatment. This type of automation can aid radiologists by doing the tedious, time consuming work. Photo by Dave Fornell

8 trends in radiology technology to watch in 2023

Here is a list of some key trends in radiology technology from our editors based on our coverage of the radiology market.

January 18, 2023
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Bayer acquires AI solutions provider Blackford Analysis

The Edinburgh-based business made the announcement on Jan. 18, noting that the acquisition will build on the company’s goals to “improve the lives of patients and populations by unlocking the adoption and benefits of medical imaging AI.” 

January 18, 2023

'Reflexive management' of incidental findings causes more harm than good

While there are numerous formulas to help guide providers in managing incidental findings, there is limited data available on the outcomes and cost-effectiveness of the subsequent evaluations that follow.

January 12, 2023
EHR-EMR

How EHR 'choice architecture' for imaging could be wasting time and money

When choosing and implementing an electronic health record system, it is important to consider how the system’s architecture might affect providers’ decision-making. 

January 10, 2023
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What radiologists should consider before taking on a remote position

Radiology is one of the few medical specialties that allow for location flexibility, but what clinicians gain in the convenience of working from home, they lose in the form of fulfillment that only physical presence can offer, some argue. 

January 9, 2023
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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
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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

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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