One-stop-shop for artificial intelligence applications launched.
CHICAGO - Finding and purchasing AI imaging applications just got a bit easier.
IBM Watson Health Imaging announced the launch of its Imaging AI Marketplace at this year’s annual meeting of the Radiological Society of North America in Chicago. The single-source solution marketplace is designed to help streamline the process of locating, buying, deploying, and managing the ever-growing options for AI Imaging applications. The one-stop-shop experience simplifies a process that can be complex and resource-draining.
As a carefully-controlled database of AI applications, the IBM Imaging AI Marketplace only includes solutions that have been cleared by the U.S. Food & Drug Administration, as well as AI solutions developed by Watson Health. This vetted catalog offers solutions for a wide range of specialties and modalities to meet customer needs.
In addition to this extensive collection of applications, the Imaging AI Marketplace also helps customers deploy AI imaging applications into their existing clinical workflow, potentially minimizing disruptions to physician productivity, and maximizing patient care.
Customers can access the Imaging AI Marketplace via IBM Watson Health’s iConnect™ Enterprise Archive.
To date, IBM Watson Health Imaging has several vendor partners for its Imaging AI Marketplace. So far, the company has partnered with Circle Cardiovascular Imaging, DiA Imaging Analysis Ltd., MaxQAI, Quantib BV, VIDA LungPrint.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).