Tools enable radiologist access to artificial intelligence.
CHICAGO - New tools are now available to help radiologists and clinicians access artificial intelligence (AI) results directly within their existing workflows.
At this year’s annual meeting of the Radiological Society of North America in Chicago, Fovia AI, Inc., a zero-footprint, cloud-based imaging SDK provider, released its XStream® aiCockpit™ and Xstream® aiPlatform™.
According to company data, XStream aiCockpit helps facilitate the connection between AI algorithms and PACS. It gives radiologists access to AI-driven workflows and visualizations that can be launched from any PACS, work list, dictation software, or hospital system. The software is 2D and 3D, and it’s accessible as a white-label product or through native integration. It also allows for efficient navigation and substantive interactions with AI results, real-time interactive tools, and local population validations.
As a vendor-neutral ecosystem, XStream aiPlatform connects AI developers, PACS, and hospital systems for effective delivery of AI-driven tools to radiologists. By selecting the algorithms that are made available, controlling the AI results flow, and letting those results be viewed with rich visualization capabilities, the product allows PACS to maintain control over the AI workflow.
XStream aiCockpit and XStream aiPlatform are compatible with several products already existing within the Fovia AI portfolio.
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).