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Products Carebot AI Bones

Vendor source

Reviewed 11 May 2026

Completeness Strong (78%)

Carebot AI Bones

CE

Carebot AI Bones is a deep learning-based decision support software that assists radiologists in interpreting X‑ray images of the appendicular skeleton (upper/lower limbs and pelvis) in patients aged ≥1 year. It analyzes musculoskeletal X-rays, highlights suspected fractures, assigns image/study risk scores, and generates a preliminary text report. The software is designed to be used in combination with PACS and DICOM viewers.

Supported by 2 papers

Carebot AI Bones

Carebot AI Bones

Carebot AI Bones

Carebot AI Bones

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Key Features

Fracture detection
Textual report generation
Worklist prioritization

General Information

Product name

Carebot AI Bones

Company

Modality

X-ray

Main task

Detection

Subspeciality

MSK

Disease targeted

Bone fracture

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIa

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

RETROSPECTIVE VALIDATION OF AN AI ALGORITHM FOR AUTOMATED BONE AGE ASSESSMENT IN PAEDIATRIC HAND RADIOGRAPHS

Medsoft · Oct 2025

Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study

Lecture Notes in Electrical Engineering · Apr 2025

Other Articles

Comparison of Diagnostic Accuracy Between Artificial Intelligence Systems and Junior Radiologists in Fracture Detection: A Retrospective, Multi-Reader Study (MEDSOFT 2025)

Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study (ECR 2025 Poster)

Assessing Artificial Intelligence and Radiologist Performance in Musculoskeletal Fracture Detection: Multi-Reader, Multi-Case Study (ECR 2025 Poster)

Cross-Center Validation of Deep Learning Model for Muskuloskeletal Fracture Detection in Radiographic Imaging: A Feasibility Study (ECR 2024 Poster)

Technical Specifications

Patient population age

Adult, Pediatric

Input

Not specified

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, DICOM GSPS, DICOM SC, DICOM-SR

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, On demand, triggered by a user through e.g. a button click, image upload

Processing time

10 - 60 seconds

Population

Patients aged 1 years or older

Market Presence

On market since

10-2025

AI Platforms

Resellers

Not specified

Countries present

10

Paying clinical customers

100+

Research/test users

30+

Pricing Information

Pricing model

Subscription

Based on

Number of analyses, Number of installations, Number of users

Source: Vendor | First published: Oct 21, 2025 | Last updated: May 11, 2026