Carebot AI CXR

Carebot

Carebot 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 seven key radiological findings, including atelectasis, consolidation, cardiomegaly, pleural effusion, pulmonary lesions, pneumothorax, and subcutaneous emphysema.

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.

Information source: Vendor
Last updated: April 15, 2025

General Information

General
Product name	Carebot AI CXR
Company	Carebot
Subspeciality	Chest
Modality	X-ray
Disease targeted	Atelectasis, consolidation, cardiomegaly, pleural effusion, pneumothorax, pulmonary lesion, subcutaneous emphysema
Key-features	Radiologic abnormal finding detection, textual report generation, worklist prioritisation
Suggested use

Technical Specifications

Data characteristics
Population	Patients aged 18 years or older
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 GSPS, DICOM SR, DICOM DOC, DICOM SC
Technology
Integration	Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone webbased
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

Regulatory

Certification
CE	Certified, Class IIa , MDR
FDA	No or not yet
Intended Use Statements
Intended use (according to CE)	Carebot AI CXR is a medical device that employs artificial intelligence, specifically machine learning and computer vision algorithms, to detect abnormalities in chest X-ray images. The software is used as a second reading system for image documentation in standard clinical practice. Carebot AI CXR is a decision support system (classification 'prediction, software that is intended to provide information for decision-making for diagnostic or therapeutic purposes') based on computer-aided detection. The software is intended for use only by trained personnel with the required competence. Image evaluation may only be performed on certified radiology monitors and in accordance with the manufacturer's recommendations. The final diagnosis and possible treatment are always determined by the physician.

Market

Market presence
On market since	03-2024
Distribution channels
Countries present (clinical, non-research use)	10
Paying clinical customers (institutes)	100+
Research/test users (institutes)
Pricing
Pricing model	Pay-per-use, Subscription
Based on	Number of users, Number of installations, Number of analyses

Evidence

Evidence
Peer reviewed papers on performance	Detecting Pulmonary Lesions in Low-Prevalence Real-World Settings Using Deep Learning (read) 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 (read) Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance (read)
Non-peer reviewed papers on performance	Enhancing Efficiency in Low-Risk Chest X-ray Reporting: A Comparative Study of Manual, Template-Based, and AI-Generated Methods (read) 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 (read)
Other relevant papers	The Effect of Computer-Assisted Diagnosis on the Accuracy of Chest X-ray Evaluations: A Crossover Study (ECR 2025 poster) (read) 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) (read) Detection of Lung Parenchymal Lesions in a Low-Prevalence Clinical Setting Using Deep Learning (ECR 2024 poster) (read) Leveraging Deep Learning Decision-Support System in Specialized Oncology Center (ECR 2023 poster) (read)