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.

Deep learning model halves lung tumor segmentation times
In a new clinical study, the model was able to maintain its performance on scans completed on different types of CT equipment across multiple medical centers.
'Ad hoc' shifts and radiology 'maestros': How one department addressed its workflow hiccups
The radiologist maestro assesses colleagues' needs in real-time and makes decisions accordingly at Children’s Hospital of Philadelphia.
Prompts matter: How input elements affect large language model performance
Just like radiologists, the performance of large language models improves when given proper context.
AI can help radiology standardize image exam data labeling
To fully leverage today's radiology IT systems, standardization is a necessity. Steve Rankin, chief strategy officer for Enlitic, explains how artificial intelligence can help.
ACR partners to create AI foundational model assessment website
Christoph Wald, MD, vice chair of the American College of Radiology Board of Chancellors, explains the partnerships with academic institutions to create the Health AI Challenge will help provide a better understanding of how foundational AI models work.

DL model identifies and segments lung tumors on CT scans.

Deep learning model halves lung tumor segmentation times

In a new clinical study, the model was able to maintain its performance on scans completed on different types of CT equipment across multiple medical centers.

Radiology "maestro" addresses department workflow hiccups.

'Ad hoc' shifts and radiology 'maestros': How one department addressed its workflow hiccups

The radiologist maestro assesses colleagues' needs in real-time and makes decisions accordingly at Children’s Hospital of Philadelphia.

artificial intelligence healthcare industry digest

Prompts matter: How input elements affect large language model performance

Just like radiologists, the performance of large language models improves when given proper context.

Video of Steve Rankin, chief strategy officer for Enlitic, explaining how AI can help standardize labeling of medical images.

AI can help radiology standardize image exam data labeling

To fully leverage today's radiology IT systems, standardization is a necessity. Steve Rankin, chief strategy officer for Enlitic, explains how artificial intelligence can help.

Video Christoph Wald explains how the Health AI Challenge help understand how foundational AI models work

ACR partners to create AI foundational model assessment website

Christoph Wald, MD, vice chair of the American College of Radiology Board of Chancellors, explains the partnerships with academic institutions to create the Health AI Challenge will help provide a better understanding of how foundational AI models work.

Commercially available AI increases breast cancer detection by nearly 20%

Results from the world’s largest prospective artificial intelligence study revealed the system could significantly benefit breast cancer screening programs.

Christoph Wald, MD, vice chair of the ACR Board, explains the new ACR Assess-AI national data registry tracks performance of clinical AI algorithms.

ACR Assess-AI national data registry tracks performance of clinical algorithms

Christoph Wald, MD, vice chair of the ACR Board of Chancellors, explains how the new Assess-AI National Radiology Data Registry is designed to help monitor accuracy and other metrics for radiology artificial intelligence.

technologist remote scanning Philips Radiology Operations Command Center ROCC

Imaging leaders share 7 key considerations for remote scanning programs

A new AHRA report provides an in-depth overview of concerns related to remote operations, highlighting everything from safety issues to regulatory oversight.

Around the web

Radiology Business

AI can help radiology standardize image exam data labeling

To fully leverage today's radiology IT systems, standardization is a necessity. Steve Rankin, chief strategy officer for Enlitic, explains how artificial intelligence can help.

Radiology Business

The biggest obstacles facing radiology business managers in 2025

RBMA President Peter Moffatt discusses declining reimbursement rates, recruiting challenges and the role of artificial intelligence in transforming the industry.

Cardiovascular Business

New cardiac prevention paradigm explored in TRANSFORM trial using AI and CCTA

Deepak Bhatt, MD, director of the Mount Sinai Fuster Heart Hospital and principal investigator of the TRANSFORM trial, explains an emerging technique for cardiac screening: combining coronary CT angiography with artificial intelligence for plaque analysis to create an approach similar to mammography.