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.

RamSoft and Alpha Nodus partner to streamline imaging prior authorization processes
RamSoft, a leader in cloud-based RIS/PACS radiology solutions, and Alpha Nodus, a developer of AI-driven administrative tools for medical offices, announced their partnership on Wednesday.
MRI-based Node-RADS effectively improves staging of head and neck cancer
Use of the scoring system could offer providers valuable preoperative insight and help guide them in surgical decisions.
What radiologists think about using ChatGPT and AI in breast imaging
Manisha Bahl, MD, explained that ChatGPT and other large language models offer significant potential to help radiologists with breast imaging exams, but they are "not quite ready for primetime."
AI beats standard regression models at predicting lung cancer risk
Not all AI or regression models are the same, nor do they all incorporate the same data when assessing patient risk.
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.

merger acquisition handshake deal business agreement partnership

RamSoft and Alpha Nodus partner to streamline imaging prior authorization processes

RamSoft, a leader in cloud-based RIS/PACS radiology solutions, and Alpha Nodus, a developer of AI-driven administrative tools for medical offices, announced their partnership on Wednesday.

MRI-based Node-RADS effectively improves staging of head and neck cancer

Use of the scoring system could offer providers valuable preoperative insight and help guide them in surgical decisions.

Manisha Bahl, MD, breast imaging division quality director and breast imaging division co-service chief, Massachusetts General Hospital, and an associate professor of radiology, Harvard Medical School, explains the findings of a recent study she was involved in at RSNA 2024. She also offers insights into growing interest at sessions in using AI in breast imaging.

What radiologists think about using ChatGPT and AI in breast imaging

Manisha Bahl, MD, explained that ChatGPT and other large language models offer significant potential to help radiologists with breast imaging exams, but they are "not quite ready for primetime."

AI beats standard regression models at predicting lung cancer risk

Not all AI or regression models are the same, nor do they all incorporate the same data when assessing patient risk.

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.

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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