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Reviewed 11 May 2026

Completeness Strong (69%)

ChestEye Quality

ChestEye Quality is an automatic Quality Assurance (QA) chest X-ray solution. It automatically double-reads all chest x-rays, by analyzing radiologists' reports and comparing them with AI analysis of the studies. If ChestEye Quality identifies possible False Negative (FN) made by radiologists, it highlights the cases for the final review for the radiologists. ChestEye Quality goal is to improve the reporting accuracy of the radiologist and act as a safety net to avoid any possible FN for the radiologists.

Supported by 5 papers

Key Features

Identifying missed clinical significant findings
Quality assurance

General Information

Product name

ChestEye Quality

Company

Oxipit

Modality

X-ray

Main task

Not specified

Subspeciality

Chest

Disease targeted

Algorithms supports 75 different pathologies

Regulatory Information

CE Certification

Pathway:

MDD

Class:

Class IIa

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Evaluating the Diagnostic Accuracy of an Artificial Intelligence Tool for Pulmonary Abnormalities in Chest Radiographs: A Retrospective Study

Cureus · Sep 2025

Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software

The Lancet Digital Health · Jul 2024

Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation

European Radiology · Mar 2024

Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings

Scientific Reports · Mar 2024

Comparison of Commercial AI Software Performance for Radiograph Lung Nodule Detection and Bone Age Prediction

Radiology · Jan 2024

Technical Specifications

Patient population age

Not specified

Input

PA or PA + LAT Digital Chest X Ray

Input format

DICOM

Output

List of possible False Negatives made by radiologists

Output format

DICOM SR or HL7 message

Integration

Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform

Deployment

Cloud-based, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload

Processing time

< 10 seconds

Population

Patients over 18 years old

Market Presence

On market since

09-2021

AI Platforms

Alma Health Platform, Blackford Analysis, CARPL.AI, deepc, HCKonnect, Philips Healthcare, Sectra

Resellers

Not specified

Countries present

Paying clinical customers

Not specified

Research/test users

Not specified

Pricing Information

Pricing model

Subscription

Based on

Number of analyses, Number of installations

Source: Vendor | First published: Sep 4, 2024 | Last updated: May 11, 2026