Health IT

Healthcare information (HIT) systems are designed to connect all the elements together for patient data, reports, medical imaging, billing, electronic medical record (EMR), hospital information system (HIS), PACS, cardiology information systems (CVIS)enterprise image systemsartificial intelligence (AI) applications, analytics, patient monitors, remote monitoring systems, inventory management, the hospital internet of things (IOT), cloud or onsite archive/storage, and cybersecurity.

Academic health system working on AI dictation feature for Epic

Chicago-based Rush University System for Health is collaborating with Suki on its development. The two previously partnered on a pilot for an AI clinician assistant, which has since been rolled out across the health system.

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. 

cyberattack cybersecurity IT

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.

PocketHealth's Image Readers helps patients understand their radiology report findings.

Newly launched report feature provides patients with a visual aid to their imaging

The artificial intelligence-enabled feature can be integrated directly into patients’ imaging results.  

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New algorithm models radiologists' eye movements to interpret chest X-rays

The algorithm has an edge over standard black box-style artificial intelligence applications because providers are able to see how it reaches conclusions.