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.

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
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
probably benign abnormalities on mammogram

Google Health partners with iCAD in commercial AI imaging push

The deal is the first commercial partnership for Google Health to introduce its breast imaging AI into clinical practice.

December 1, 2022
An example of commercially available artificial intelligence (AI) automated grading of breast density on mammograms from the vendor Densitas..

VIDEO: Role of AI in breast imaging with radiomics, detection of breast density and lesions

Connie Lehman, MD, chief of breast imaging, co-director of the Avon Comprehensive Breast Evaluation Center at Massachusetts General Hospital, discusses how artificial intelligence (AI) is being implemented in breast imaging.

October 18, 2022
Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, explains some of technology trends KLAS researchers have found in enterprise imaging system and radiology artificial intelligence (AI).

VIDEO: KLAS shares trends in enterprise imaging and AI

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, explains some of technology trends KLAS researchers have found in enterprise imaging system and radiology artificial intelligence (AI).

October 13, 2022
Charles E. Kahn, Jr., MD, MS, Editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine. He has been heavily involved in radiology informatics and has seen up close the evolution of radiology toward deeper integration with AI. #RSNA

VIDEO: Use cases and implementation strategies for radiology artificial intelligence

Charles Kahn, Jr., MD, editor of the the journal Radiology: Artificial Intelligence, and professor and vice chair of radiology at the University of Pennsylvania Perelman School of Medicine, explains the work involved integrating AI in radiology systems and the role of AI in augmenting patient care.
 

October 12, 2022
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.  

October 11, 2022
Cardiovascular information systems (CVIS) combine imaging and reporting into one system that allows access across the cardiovascular service line. Here are 7 trends in CVIS according to KLAS.

VIDEO: 7 trends in cardiovascular information systems seen by KLAS

Monique Rasband, vice president of imaging, cardiology and oncology, KLAS Research, explains a few of the key technology trends in cardiovascular information systems (CVIS).

September 30, 2022

Around the web

The agency is urging healthcare providers to transition away from these devices and seek out alternatives. It is even working with other manufacturers to try and get similar products on the market as quickly as possible. 

Jeffrey Kuvin, MD, one of the leading voices behind efforts to create a new Board of Cardiovascular Medicine, spoke with Cardiovascular Business about where things stand today.

Half a year after President Biden officially directed federal agencies in the executive branch’s bailiwick to “seize the promise and manage the risks” of AI, the White House has posted a status report.

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