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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Breast density notification requirements officially go into effect

Imaging facilities are now required to notify all women about their breast density status after completing a mammogram.  

artificial intelligence robot evaluates healthcare data

Healthcare AI company launches radiology-specific vision language model

Harrison.rad.1 can conduct chats related to imaging, identify and localize X-ray findings, generate reports and provide its reasoning based on patient history and clinical context.

ChatGPT large language models radiology health care

GPT-4 is better at explaining IR procedures than physicians

The demand for interventional radiology procedures is growing, but it is a specialty for which health literacy is lacking.

Video interview with ACR CEO Dana Smetherman, MD, who explains how the American College of Radiology can help radiology practices evaluate and vet AI.

ACR offers resources to achieve radiology AI best practices

Dana Smetherman, MD, CEO of the American College of Radiology, explains resources available through its Data Science Institute to evaluate and validate the quality of imaging algorithms.

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EHR intervention cuts unnecessary MRI orders by 35%

Many of these exams are ultimately deemed normal, and their results often do not affect how patients’ headaches are managed. 

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Why PACS should be part of undergrads' medical education

Many traditional radiology courses leave out hands-on opportunities for students—something that could greatly benefit their understanding of the specialty.

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.

PHOTO GALLERY: Examples of FDA-cleared AI in radiology

This is a photo gallery of artificial intelligence products cleared for clinical use in medical imaging by the U.S. Food and Drug Administration. Radiology by far is the leader of all clinical AI FDA approvals.

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ChatGPT's medical writing is getting so good that it may soon fool AI detectors

The large language model’s medical manuscripts are becoming so well constructed that it can be difficult to distinguish them from those compiled by humans. 

Around the web

GE HealthCare's flurpiridaz, the PET radiotracer that recently received FDA approval, offers several key benefits over SPECT. Jamshid Maddahi, MD, discussed the details in an exclusive interview. 

Ultrafast MCE could go on to become a go-to treatment option for obstructive coronary artery disease, according to the authors of a new first-in-human clinical study.

Elucid's PlaqueIQ was trained to turn CCTA images into interactive 3D reports that help physicians visualize the presence of atherosclerosis.

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