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.
Information source: Vendor
Last updated: Dec. 18, 2023

General Information

Product name ChestView
Subspeciality Chest
Modality X-ray
Disease targeted Pneumothorax, pleural effusion, alveolar syndrome, nodule, mediastinal mass
Key-features Triage, detection and localization of pneumothorax, pleural effusion, alveolar syndrome, nodule, mediastinal mass, worklist prioritization
Suggested use Before: adapting worklist order, flagging acute findings,
During: perception aid (prompting all abnormalities/results/heatmaps)
After: diagnosis verification

Technical Specifications

Data characteristics
Population All Chest X-rays
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 Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis Automatically, right after the image acquisition
Processing time 10 - 60 seconds


Certified, Class IIa , MDR
FDA No or not yet
Intended Use Statements
Intended use (according to CE) Software intended to provide preliminary data for helping physicians’ diagnosis of body X-rays


Market presence
On market since 05-2021
Distribution channels Blackford,, deepcOS, Incepto, Inframed, Osimis, Papapostolou, Philips AI Manager, RMS Medical Devices, Sectra Amplifier Store, Softway, Eureka Clinical AI, Aidoc aiOS
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing model Pay-per-use, Subscription
Based on Number of installations, Number of analyses


Peer reviewed papers on performance

  • Using AI to Improve Radiologist Performance in Detection of Abnormalities on Chest Radiographs (read)

Non-peer reviewed papers on performance

  • Abstract ECR 2023: Evaluation of radiologists’ performance compared to a deep learning algorithm for the detection of thoracic abnormalities on chest X-ray (read)

Other relevant papers