Health AI Register Logo
ProductsPlatformsCompaniesNewsContact
ProductsChestView

Vendor source

Reviewed 11 May 2026

Completeness Strong (78%)

ChestView

GLEAMER

ChestView detects and localizes lesions on Chest X-rays. It is designed to assist radiologists and clinicians in triaging cases and increasing diagnostic performances by highlighting regions of interest with a plain box (> 90% confidence) or a dotted box (50-90% confidence) and providing a summary table.

Supported by 6 papers

ChestView

Key Features

  • Consolidation
  • Detection and localization of pneumothorax
  • Mediastinal or hilar mass
  • Nodule
  • Pleural effusion
  • Triage
  • Worklist prioritization

General Information

Product name

ChestView

Company

Modality

X-ray

Main task

Not specified

Subspeciality

Chest

Disease targeted

Pneumothorax, pleural effusion, consolidation, nodule, mediastinal or hilar mass

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIa

Not verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Novel Artificial Intelligence Chest X-ray Diagnostics: A Quality Assessment of Their Agreement with Human Doctors in Clinical Routine

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · Jan 2026

Performance Comparison Between Two Versions of a Commercial Artificial Intelligence System for Chest Radiograph Interpretation: A Multicenter Study

Journal of Imaging Informatics in Medicine · Nov 2025

Opinion paper: artificial intelligence as a clinical ally in lung cancer detection—the case for Gleamer’s ChestView

Frontiers in Oncology · Oct 2025

Efficacy of a deep learning-based software for chest X-ray analysis in an emergency department

Diagnostic and Interventional Imaging · Apr 2025

Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs

Radiology · Dec 2023

Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation

European Radiology · Aug 2023

Technical Specifications

Patient population age

Not specified

Input

Chest X-rays AP, PA, lateral, bed side

Input format

DICOM

Output

Summary table, bounding boxes showing regions of interest

Output format

DICOM SC

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

Processing time

10 - 60 seconds

Population

Adults and Children (> 15 years old)

Market Presence

On market since

05-2021

Resellers

RMS Medical Devices

Countries present

>36

Paying clinical customers

>350

Research/test users

Not specified

Pricing Information

Pricing model

Subscription

Based on

Number of analyses, Number of installations

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