Imaging Informatics

Imaging informatics (also known as radiology informatics, a component of wider medical or healthcare informatics) includes systems to transfer images and radiology data between radiologists, referring physicians, patients and the entire enterprise. This includes picture archiving and communication systems (PACS), wider enterprise image systems, radiology information. systems (RIS), connections to share data with the electronic medical record (EMR), and software to enable advanced visualization, reporting, artificial intelligence (AI) applications, analytics, exam ordering, clinical decision support, dictation, and remote image sharing and viewing systems.

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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