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Home » F.A.S.T.® aiCockpit® CT Lung Nodule Software Receives FDA 510(k) Clearance
Press Release

F.A.S.T.® aiCockpit® CT Lung Nodule Software Receives FDA 510(k) Clearance

By News RoomMarch 3, 20253 Mins Read
F.A.S.T.® aiCockpit® CT Lung Nodule Software Receives FDA 510(k) Clearance
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Provides Efficient Interaction with Consolidated AI Results

PALO ALTO, Calif., March 03, 2025 (GLOBE NEWSWIRE) — Fovia Ai, Inc., an innovative leader in AI findings analysis and workflow software, has received FDA 510(k) clearance for F.A.S.T.® aiCockpit® CT Lung Nodule through Fovia, Inc., its parent company. F.A.S.T. aiCockpit CT Lung Nodule is an application that allows radiologists to efficiently visualize, navigate, edit and accept/reject lung nodule detection algorithm results.

FDA Cleared 510(k) F.A.S.T.® aiCockpit® CT Lung Nodule Software Now Available for PACS,
Universal Viewers, Reporting Systems, and AI Vendors, Orchestrators, and Platforms

F.A.S.T. aiCockpit CT Lung Nodule provides radiologists a powerful tool for reviewing AI-driven radiology findings that both accelerates case times and expedites artificial intelligence integration within the radiology landscape. This streamlined application provides optimized workflows for interacting with AI results from Chest CT Lung Nodule detection AI algorithms and is the first component of F.A.S.T. aiCockpit Universal AI Viewer, which will deliver optimized workflows for all radiology AI algorithms, further eliminating the need to launch multiple viewers.

F.A.S.T. aiCockpit CT Lung Nodule can be easily embedded in existing workflows so that radiologists can quickly review, edit and decide on the relevancy and accuracy of algorithm results. Adjustments to diameter and volume measurements, editing of descriptions and locations of detected lung nodules, rejection of false positives, and examination in 2D/3D are just a few examples of what is available within the application. Additionally, all radiologist-approved, finalized results are automatically forwarded to the long-term archive and reporting system for automated inclusion in the radiologist report.

PACS, Universal Viewers, Reporting Systems, and AI Vendors, Orchestrators, and Platforms can easily install and offer this technology within their existing products, and quickly scale their AI offering. Multiple algorithms can be included in any F.A.S.T. aiCockpit CT Lung Nodule installation, eliminating the need for companies to integrate separate AI vendors and algorithms, thereby significantly reducing integration costs. Once F.A.S.T. aiCockpit is installed, companies can immediately begin offering efficient, comprehensive AI tools resulting in faster case times, thereby increasing accuracy and expediting clinical decision-making for their radiologists.

About Fovia Ai

Fovia Ai, Inc. is a subsidiary of Fovia, Inc., a world leader in advanced visualization, a preeminent provider of cloud-based, zero-footprint imaging SDKs, and the developer of High Definition Volume Rendering®, XStream® HDVR®, F.A.S.T.® Hybrid Rendering, F.A.S.T.® Interactive Segmentation, RapidPrint® and TruRender®. Fovia Ai’s flagship products, F.A.S.T.® aiCockpit® and F.A.S.T.® AI SDK, enable radiologists and clinicians to efficiently access AI results directly within their existing workflows from any PACS, worklist, dictation software or hospital system. Complementary products in Fovia Ai’s product suite include F.A.S.T.® AI Annotation, F.A.S.T.® AI Validation, F.A.S.T.® AI Workflows and F.A.S.T.® Interactive AI, collectively providing tools to annotate, validate, modify, accept/reject, interact with and segment data. The flexible architecture of Fovia Ai’s product suite and Fovia’s 20+ years of radiology integration experience facilitate seamless integrations with a variety of partners, platforms, processors and operating systems.

For additional information and to learn more about commercial, academic or research licensing, visit fovia.ai or fovia.com.

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/7fc7b053-2005-47a3-838d-b00f015a5eab


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