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RadioEye

AI-Powered Image Retrieval Tool for Reading MRIs

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Report with More Accuracy

Our proprietary reference database contains radiological images with clinically and/or

histologically verified diagnoses, providing 

confidence in radiological findings

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Search by Images, Not by Text

Browse through a validated set of MRI cases that will help you in your diagnosis

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 One - Click
Functionality

With seamless integration into PACS, RadioEye will enhance  your workflow

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High - Quality Reference Materials

Compare your MRI case to a vast library of images with validated diagnoses

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AI-Based
Image Search

RadioEye provides images with similar patterns to help you evaluate potential differential diagnoses

The RadioEye Impact:
Report with Increased Accuracy, Confidence, and Efficiency*

+26%

Increase in Overall

Diagnostic Accuracy vs Current 

Standard of Care

+49%

Increase in Diagnostic Accuracy

When Incorporated into Standard of Care

RadioEye improved accuracy across all experience levels and perceived scan difficulties

Radiologists reported that they were more confident in their diagnosis when using RadioEye 

+92%

Increase in Diagnostic Accuracy

Among Radiologists with No Experience Reading Eye/Orbit Scans,

RadioEye added to Standard of Care

+29%

Faster Time to Diagnosis

When Using RadioEye Alone

*Based on alpha study results, May 2024. Retrospective study conducted with 36 radiologists. For more details, please refer to our study here: 

https://www.medrxiv.org/content/10.1101/2024.07.24.24310920v1

Our Mission

Our Mission is to improve patient outcomes by increasing accuracy,

confidence, and efficiency in diagnosing radiological images

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Harnessing the Power of AI to Improve Diagnostic Accuracy

For our image search tool, we train a powerful neural network on millions of MR images across different pathologies to identify similar image clusters

Collaboration Opportunities

Radiology Partners
For collaboration on product development, clinical validation, and early commercial access
Industry Partners

To explore integration synergies and distribution opportunities

Supported by

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