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Carebot AI CXR
Carebot AI CXR
Carebot AI CXR
Carebot AI CXR
Carebot AI CXR
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Key Features

  • Radiologic abnormal finding detection
  • Textual report generation
  • Worklist prioritisation

General Information

Product name

Carebot AI CXR

Company

Subspeciality

Chest

Modality

X-ray

Disease targeted

Atelectasis, consolidation, cardiomegaly, pleural effusion, pneumothorax, pulmonary lesion, subcutaneous emphysema

Main task

Not specified

Technical Specifications

Population

Patients aged 18 years or older

Patient population age

Not specified

Input

Chest X-ray PA (posterior-anterior view), chest X-ray AP (anterior-posterior view)

Input format

DICOM

Output

Localization (bounding-boxes, report), abnormality score for each finging and study, worklist order, multi-language radiology report generation

Output format

DICOM DOC, DICOM GSPS, DICOM SC, DICOM SR

Integration

Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone webbased

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

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIa

Verified by Health AI Register

Other certifications

Not specified

Market Presence

On market since

03-2024

AI Platforms

Not specified

Resellers

Not specified

Countries present

10

Paying clinical customers

100+

Research/test users

Not specified

Pricing Information

Pricing model

Pay-per-use, Subscription

Based on

Number of analyses, Number of installations, Number of users

Evidence & Research

Peer-Reviewed Papers

Peer-Reviewed

View

Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings Using Deep Learning

Peer-Reviewed

View

Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images

Peer-Reviewed

View

Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance

Technical Papers

Technical

View

Enhancing Efficiency in Low-Risk Chest X-ray Reporting: A Comparative Study of Manual, Template-Based, and AI-Generated Methods

Technical

View

Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice

Other Articles

Other

View

The Effect of Computer-Assisted Diagnosis on the Accuracy of Chest X-ray Evaluations: A Crossover Study (ECR 2025 poster)

Other

View

Evaluating the Performance of Deep Learning Model and Junior Radiologists in Reading Major Pathologies on Chest X-Rays: A Population-Based, Multi-Reader Stu (ECR 2025 poster)

Other

View

Detection of Lung Parenchymal Lesions in a Low-Prevalence Clinical Setting Using Deep Learning (ECR 2024 poster)

Other

View

Leveraging Deep Learning Decision-Support System in Specialized Oncology Center (ECR 2023 poster)

Source: vendor | First published: May 14, 2024 | Last updated: Jul 9, 2025