Vendor source
Reviewed 17 Jun 2026
Completeness Strong (75%)
Carebot AI CXR
CarebotCarebot AI CXR is a deep learning-based decision support software that assists radiologists in interpreting chest X-ray images in AP/PA projection. It serves as a secondary reader of key radiological findings, including atelectasis, consolidation, cardiomegaly, (lateral) pleural effusion, pulmonary lesions, pneumothorax, subcutaneous emphysema and external devices. Deep learning algorithms within the Carebot AI CXR detect and localize areas of interest and provide a simplified text report of the analysis result by individual finding. The software is designed to be used in combination with PACS and DICOM viewers.
Supported by 4 papers
General Information
Product name
Company
Modality
Main task
Not specified
Subspeciality
Disease targeted
Regulatory Information
Pathway:
MDR
Class:
Class IIa
Verified by Health AI Register
Other certifications
Not specified
Evidence & Research
Peer-Reviewed Papers
AI-Assisted Chest X-Ray Reading Improves Sensitivity Without Reducing Specificity: A Crossover Study
Smart Innovation, Systems and Technologies · Jan 2026
Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings Using Deep Learning
Lecture Notes in Electrical Engineering · Mar 2024
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
Diagnostics · Mar 2023
Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance
BioMedInformatics · Jan 2023
Technical Papers
Enhancing Efficiency in Low-Risk Chest X-ray Reporting: A Comparative Study of Manual, Template-Based, and AI-Generated Methods
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
The Effect of Computer-Assisted Diagnosis on the Accuracy of Chest X-ray Evaluations: A Crossover Study (ECR 2025 poster)
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)
Detection of Lung Parenchymal Lesions in a Low-Prevalence Clinical Setting Using Deep Learning (ECR 2024 poster)
Leveraging Deep Learning Decision-Support System in Specialized Oncology Center (ECR 2023 poster)
Technical Specifications
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Source: Vendor | First published: May 14, 2024 | Last updated: Jun 17, 2026