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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Nicholas Galante

AI is revolutionizing radiology workflow and patient care

Sponsored by Viz.ai

In the rapidly evolving healthcare landscape, artificial intelligence (AI) is making significant strides in improving radiology workflow and patient care coordination. Nicholas Galante, MD, medical director of informatics at Radiology Associates of North Texas, recently discussed how technology from Viz.ai is transforming his radiology practice, enhancing efficiency, and ultimately benefiting patient outcomes. 

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Radiology practice network LucidHealth signs $25M deal with vendor Visage Imaging

Under the agreement, the Columbus, Ohio, provider group will implement the cloud-based Visage 7 Enterprise Imaging Platform across its organization. 

radiology dermatology collaboration

Researchers overseas: ‘Radiology has become indispensable to dermatology’

Dermatologists increasingly rely on medical imaging modalities—especially but not solely ultrasound—to help diagnose complex and diverse skin disorders. 

VI-RADS threshold, imaging features predict bladder cancer invasiveness with nearly 100% accuracy

New findings related to Vesical Imaging-Reporting and Data System scores and specific MRI findings could improve the management of bladder cancer. 

Konica Minolta Healthcare Americas Inc. and ImagineSoftware announced an integration agreement to integrate the ImagineOne artificial intelligence (AI)-driven platform for automated radiology billing with Konica Minolta’s Exa PACS-RIS solution.

Konica Minolta and ImagineSoftware partner to expand revenue cycle management offerings

Konica Minolta partnered with ImagineSoftware to integrate its AI-driven revenue cycle management platform into the Exa PACS-RIS solution. 

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Chinese hackers use malware disguised as imaging viewers to steal patient data

The software has been primarily disguised as Philips’ DICOM MediaViewerLauncher.exe—a trusted program that enables patients to view their medical imaging on their own personal servers. 

Jason Poff, MD, director of innovation deployment for artificial intelligence (AI) at RadPartners, explains the five-step process he uses to evaluate medical imaging AI.

5 steps for evaluating radiology AI applications

Jason Poff, MD, director of innovation deployment for artificial intelligence at Radiology Partners, explains the process he uses to evaluate medical imaging AI. 
 

AI detects subtle changes in images over time.

Adaptable AI system detects subtle changes in imaging, has potential across multiple clinical settings

The Learning-based Inference of Longitudinal imAge Changes, or LILAC, system harnesses machine learning to review medical images that have been collected over a prolonged period.

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