Underpinning our Collaborative imaging approach is a commitment to creating smart solutions, powered by AI, that deliver uncompromised quality and value across the entire care pathway. Using only the smartest innovations, including deep learning technology, our goal is to help clinicians improve patient outcomes through the provision of:
Our solutions have been designed to enhance your clinical confidence with high-quality images and applications that help you make informed treatment decisions in real-time.
We have created simple, streamlined AI-driven workflows that optimize resource deployment and ensure your teams have the insights they need to work smarter every day.
We’re channeling our focus into the provision of AI solutions that enable your patients to get the fast, accurate results they need for a more confident and personalized approach to care.
Up to 82.4%** dose reduction with Advanced intelligent Clear-IQ Engine (AiCE)
AiCE is applicable to >98% of all CT, MR & PET procedures*
We’re obsessed with the pursuit of smarter, faster, better imaging solutions that can transform the way you work.
We oversee a range of research projects to ensure you’re always working with the latest insights available.
Dr. Eliot Siegel
Professor and Vice Chair Research Informatics, University of Maryland School of Medicine, Chief of Radiology and Nuclear Medicine, Veterans Affairs Maryland Healthcare System
“AI in Medical Imaging: Hype, Myth, Reality and Next Steps.”Watch Video
Dr. Russell Bull
Royal Bournemouth Hospital, UK
“This year, we saw how AI can actually improve the images.”Watch Video
Dr. Peter Chang
Co-Director of University of California at Irvine (UCI) Center for AI in Diagnostic Medicine
“AI has the power to dramatically impact patient care.”Watch Video
Dr. Rasu Shrestha
Atrium Health, USA
“It humanizes healthcare and connects us with our patients.”Watch Video
* Based on the IMV report on total CT, MR & PET Procedure Volumes in 2019. Excludes CT Fluoro and non-oncolology PET imagined
* From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Market Expansion in Healthcare
** A Model Observer study was performed comparing AiCE to FBP. Actual clinical results may differ depending on the clinical task, patient size, anatomical location and clinical practice.
** Based on the detectability index performance metric, a measure of signal to noise that takes into account the magnitude and texture of both the signal and the noise for a given LCD task.
** On the Aquilion ONE Genesis Edition, a model observer evaluation showed that equivalent low contrast delectability to FBP (range from 0.649 - 0.695) can be achieved with 79.6 to 82.4% less dose using AiCE at Standard setting for thin (0.5 mm) reconstruction slice thickness in simulated body phantom (MITA-FDA phantom with a body ellipse surrounding it).