BoneView
by Gleamer
· Based in France →
— AI solution that detects bone trauma on X-rays with expert-level performance, improving diagnostic confidence in trauma care.
Contact for pricing
Regulatory Status Disclosed
Overview
BoneView by Gleamer is an advanced AI-driven software designed to assist clinicians in the interpretation of X-ray radiographs for the detection of bone trauma. It identifies fractures, effusions, dislocations, and bone lesions across various anatomical regions, including limbs, pelvis, rib cage, and thoracic and lumbar spine. The tool provides pre-diagnosis labels (POSITIVE, DOUBT, NEGATIVE) with confidence levels, highlighting suspected regions with bounding boxes on images.
Integrated seamlessly into existing radiology reading workflows, BoneView aims to enhance diagnostic accuracy, reduce reading time, and optimize workflow efficiency. It supports preliminary and second reads, helps triage urgent cases through worklist prioritization, and can generate draft patient reports automatically with its AutoReport function.
Clinically validated, BoneView has demonstrated its ability to reduce missed fractures and improve sensitivity and specificity for both radiologists and non-radiologists. It is intended for use by radiologists, orthopedic surgeons, emergency physicians, rheumatologists, family physicians, and physician assistants.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered fracture detection
- Detection of dislocations and bone lesions
- Detection of effusions
- Worklist prioritization based on AI findings
- AutoReport function for draft report generation
- Image annotation with bounding boxes and confidence labels
- Seamless integration with PACS and RIS
- Support for adult and pediatric patients (over 2 years old for limbs)
- Covers limbs, pelvis, rib cage, thoracic and lumbar spine X-rays
Use Cases
- Preliminary reads for X-ray trauma exams
- Second reads to enhance diagnostic accuracy
- Fracture and bone-trauma detection in emergency departments
- Optimizing radiology workflow and reducing reading time
- Triaging studies to prioritize urgent cases
- Supporting residents and clinicians in fracture detection
What Physicians Need to Know
DICOM Support & Standards
BoneView supports DICOM, DICOM SR, and GSPS standards. It also supports HL7 v2 and FHIR for data exchange.
PACS Integration Method
BoneView integrates into standard reading environments, including PACS and RIS. It can be deployed on-premises or in the cloud, connecting to PACS or X-ray systems as the DICOM source. BoneView receives DICOM images, processes them, and generates result files in DICOM format, which include a summary table and annotated images, sending them back to the DICOM destination. It can also push secondary images with AI findings and a summary table into PACS. BoneView runs via Sectra Amplifier Services and integrates into Sectra PACS.
Reading Room Workflow Impact
BoneView is designed to be a concurrent reading aid, highlighting regions of interest with bounding boxes around suspected fractures to help radiologists prioritize reading those X-rays. It aims to enable faster and more accurate film reading within the existing process without requiring new equipment. Studies have shown it can reduce reading time by 15% overall and up to 36% in some studies, while improving diagnostic accuracy and reducing missed fractures by nearly 30%. BoneView provides pre-diagnosis labels (positive, doubt, negative) and can perform worklist prioritization. It can also generate draft reports using its AutoReport function.
AI Model Architecture (deep learning approach)
BoneView utilizes deep learning algorithms. The algorithm is based on the 'Detectron 2' framework and was developed using a dataset of 60,170 trauma radiographs from 22 institutions.
Processing Speed (per study)
The system can return a preliminary diagnosis within less than three minutes. Other sources indicate processing times between 1-10 minutes.
FDA Clearance Pathway (510k/De Novo)
BoneView received FDA 510(k) clearance. It was initially cleared for adults in March 2022 and later received 510(k) clearance for pediatric patients (over two years of age) in March 2023.
Supported Modalities (CT/MRI/X-ray/US)
BoneView is designed for X-ray images.
Sensitivity & Specificity Data
In a multi-reader, multi-case study, aided readers' sensitivity rose from 0.648 to 0.752 and specificity from 0.906 to 0.956. Standalone tests on 8,918 adult radiographs showed sensitivity of 0.928 and specificity of 0.811 at a high sensitivity operating point, and 0.841 sensitivity and 0.932 specificity at a high specificity point. For pediatric radiographs, sensitivity was 0.909 and specificity 0.821 at high sensitivity, and 0.792 sensitivity and 0.965 specificity at high specificity. One study showed an increase in sensitivity by 10.4% and a reduction in reading time by 15%. Another study demonstrated a 91.3% sensitivity rate for pediatric fracture detection. AI assistance has shown to increase sensitivity for residents without loss of specificity, with assisted reports achieving 91.28% sensitivity and 97.36% specificity in one study. A meta-analysis indicated that BoneView improves reader sensitivity, with mixed results for specificity (improving for some, decreasing for others), and improves reading speed.
RSNA/ACR Validation
The work on BoneView, 'Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence,' earned the Alexander R. Margulis Award for Scientific Excellence.
Physician Tip
BoneView can serve as a valuable concurrent reading aid, especially in high-volume settings like emergency departments, to reduce missed fractures and improve reading efficiency. Pay attention to the AI's confidence labels (positive, doubt, negative) and utilize the worklist prioritization feature. While the AI improves sensitivity, always correlate findings with clinical context and patient history. The AutoReport function can help streamline documentation. Remember that while BoneView is cleared for both adults and children over two years old, understanding its performance across different anatomical regions and age groups can optimize its use.
BoneView offers flexible integration options, including direct PACS/RIS integration, on-premises or cloud deployment, and compatibility with various X-ray systems. It supports key standards like DICOM, HL7, and FHIR, facilitating integration into existing hospital IT infrastructure. It is also available through imaging analysis platforms such as Fujifilm, Aidoc, Ferrum Health, and Blackford Analysis.
Details
| Category | Radiology & Imaging AI |
| Pricing | Contact for pricing |
| Deployment | Cloud-based, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker) |
| Compliance | |
| BAA Available | Yes AI-estimated |
| HIPAA Compliant | Yes AI-estimated |
| FDA Status | Cleared AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Emergency Medicine, Orthopedics, Radiology |
What the Web Says
BoneView, developed by Gleamer.ai, is an AI-powered solution designed to detect bone trauma, including fractures, effusions, dislocations, and bone lesions, on X-ray images. It aims to improve diagnostic accuracy and efficiency in emergency and musculoskeletal care by providing a second reading and highlighting suspicious areas. Physicians and healthcare institutions generally view BoneView as a valuable tool that enhances patient care and streamlines workflows, particularly in situations with high workloads or limited radiologist availability.
Overall: PositiveStrengths
- Improves fracture detection sensitivity and reduces missed fractures by up to 30%.
- Reduces reading time for radiologists and emergency physicians.
- Acts as a reliable second opinion, increasing diagnostic confidence, especially for less experienced physicians or during fatigue.
- Automates report generation, saving time and ensuring consistency.
- Integrates with existing healthcare systems and workflows (PACS).
- Supports faster patient assessment and can help optimize emergency department flow.
Limitations
- Specificity results can be mixed, improving for some readers and decreasing for others.
- Limited accuracy for complex scenarios such as multiple fractures and dislocations.
- Not yet robust enough for stand-alone diagnosis and should primarily assist, rather than replace, radiologists.
- Performance can vary between studies, indicating some heterogeneity.
- May have lower accuracy than senior radiologists in detecting certain complex injuries.
- Requires further improvement for clinicians to rely solely on AI-based pediatric fracture detection.
Based on reviews from: prezi.com, researchgate.net, omnius.so, smw.ch, testdynamics.net, nih.gov, biospace.com, gleamer.ai, auntminnie.com, elion.health, blackfordanalysis.com, wrnjradio.com, sevendaysvt.com, cnblogs.com, bestdevops.com, ai-pedias.com
Last updated: 2026-10-30
Ratings & Reviews
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Press & Coverage
gleamer.ai
Gleamer partners with Siemens Healthineers: BoneView now available on the Siemens Healthineers Digital Marketplace
Gleamer's BoneView AI Copilot is now accessible on the Siemens Healthineers Digital Marketplace, offering healthcare providers a curated catalog of digital health innovations to modernize care and improve patient outcomes. This partnership aims to expand the reach of BoneView, allowing more institutions to integrate its AI-powered fracture detection capabilities into their workflows.
2024-09
gleamer.ai
Konica Minolta Healthcare and Gleamer Announce Strategic Partnership to Deliver AI-Powered Bone Fracture Detection to the Americas
Konica Minolta Healthcare has partnered with Gleamer to offer BoneView with its DR product portfolio in the Americas, including various X-ray systems. This collaboration aims to provide scientifically validated AI-based fracture detection to a wider customer base, enhancing diagnostic accuracy and reducing radiologist reading times.
2024-12
gleamer.ai
Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department
This peer-reviewed article published in the Journal of Clinical Medicine in September 2024 evaluates the effectiveness of BoneView for limb radiographic fracture recognition in emergency departments. The study contributes to the growing body of evidence supporting the use of AI in improving diagnostic accuracy in acute care settings.
2024-09
gleamer.ai
Artificial intelligence-assisted detection of fractures on radiographs with BoneView: a systematic review
Published in the European Journal of Radiology in June 2025, this systematic review by Robert M. Kwee and Thomas C. Kwee assesses the performance of BoneView in detecting fractures on radiographs. The review synthesizes data from multiple studies, indicating that BoneView generally improves the sensitivity of readers and reading speed, though specificity results are mixed.
2025-06
slhn.org
First in Region to Use AI for X-Rays at All ERs
St. Luke's University Health Network has become the first in its region to deploy Gleamer BoneView AI across all its acute care hospital ERs and urgent care centers. This technology assists in identifying bone trauma such as fractures, dislocations, effusions, and lesions, aiming to enhance diagnostic accuracy, speed, and consistency in image interpretation.
2025-09
medtechdive.com
RadNet acquires AI developer Gleamer for up to $245M
RadNet acquired Paris-based Gleamer for up to 230 million euros in March 2026, strengthening its AI-enabled imaging capabilities. Gleamer's portfolio includes BoneView, which identifies fractures on X-rays, and other products for lung nodule detection and MS lesion identification.
2026-03
philips.com
AI-powered X-ray solution saves 594 days of waiting time and prevents 13,800 emergency consultations at Vestre Viken Health Trust
Vestre Viken Health Trust in Norway implemented Gleamer's BoneView as its first AI tool in routine clinical use, leading to significant improvements in patient care. Since August 2023, the solution has saved 594 days of waiting time, prevented 13,800 emergency consultations, and freed up 3,100 physician hours.
2026-02
mdpi.com
Gleamer BoneView®: AI-Powered Fracture Detection in Emergency Orthopaedics
This pilot study, published in June 2026, evaluates the impact of Gleamer BoneView® on orthopaedists' accuracy in detecting fractures during night shifts in the ED without on-site radiologist support. The study found that while BoneView showed a non-significant reduction in diagnostic errors, prior studies have demonstrated AI's potential to enhance fracture detection sensitivity.
2026-06
Videos
Product demos, reviews, and walkthroughs for BoneView.
BoneView by GLEAMER - English Subtitles
GLEAMER
Frequently Asked Questions
BoneView is an AI-powered software developed by Gleamer designed to detect bone trauma on X-rays with expert-level performance. It provides radiologists and other clinicians with an instant and automatic second reading of trauma bone X-rays, integrating fully into the existing reading workflow. The software highlights regions of interest with bounding boxes around suspected fractures, dislocations, effusions, and bone lesions, and can also prioritize reading those X-rays.
BoneView has received US FDA clearance as a CADe/CADx (computer-assisted detection and diagnosis) device for fracture detection in adults and pediatric patients over two years old for limbs, pelvis, rib cage, and dorsolumbar vertebra radiographs. It also holds CE mark class IIa certification in the European Union and is cleared by Health Canada as a Class II medical device. Gleamer emphasizes data protection and compliance with healthcare regulations for all its AI solutions.
BoneView integrates directly into PACS, pushing secondary images with AI findings and a summary table for a quick overview. It can also perform worklist prioritization based on its pre-diagnosis labels (positive, doubtful, negative). Studies have shown that BoneView assistance can improve fracture detection sensitivity by 10.4% and shorten radiograph reading time by 6.3 seconds per patient, potentially reducing missed fractures by up to 30%.
While BoneView shows promising performance, its standalone diagnosis is not permitted, as it is intended to assist radiologists in detection and prioritization. The software primarily focuses on fracture detection and may not provide comprehensive information such as displacement, fracture shape, or joint involvement, nor does it convey other critical secondary findings. Some studies suggest that while AI improves sensitivity, specificity results can be mixed, and performance can vary between anatomical regions and for subtle abnormalities. It is also noted that AI technologies may not be suitable for certain groups, such as very young children or those with conditions affecting bone health, and there's a risk of reduced diagnostic accuracy in different populations.
Yes, there are other AI algorithms for fracture detection, such as RBfracture and Rayvolve. Comparative studies have shown that BoneView offers balanced performance among these, with comparable AUCs for acute fractures. While some competitors claim higher sensitivity and AUC, BoneView has demonstrated high diagnostic performance and almost perfect agreement with RBfracture in detecting traumatic abnormalities in MSK radiographs.
While specific pricing for BoneView is not publicly disclosed by Gleamer, AI radiology software is generally priced using models such as per-study fees (ranging from $0.50–$5 per study) or per-year/per-site licensing (typically $40K–$150K annually for a mid-sized hospital). Additional costs can include implementation, integration with PACS and EHR systems, initial training, annual maintenance, and per-modality add-ons.
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| Last Verified: 2026-04-18 | First Added: 2026-04-18
Investors who backed BoneView
Funded through the company that built this tool.
