JSON twin: https://www.healthaidb.com/software/chesteye-quality.json
Oxipit
AI-driven quality assurance tool that automatically double-reads chest X-rays by comparing radiologist reports with AI image analysis to flag potential missed findings.
ChestEye Quality (Oxipit) is an automatic QA solution for chest radiography that analyzes final radiologist reports alongside chest X-ray images using AI to identify potential false negatives, flagging cases for secondary human review and delivering alerts via PACS/RIS/HIS, email or structured messages (DICOM SR/HL7). It supports prospective and retrospective auditing, integrates into standard reading workflows, and is deployable cloud or on-premises.
yes
2017
P2075
2025-09-07
commercial/proprietary
Subscription; pricing based on number of analyses and number of installations; contact vendor for quotes and trial information
subscription
2021
product
1.0
{
"company_name": "Oxipit",
"company_url": "https://oxipit.ai/",
"company_offices": [
"Lithuania",
"United States",
"Germany",
"France",
"Finland"
],
"company_founding": "2017",
"product_url": "https://oxipit.ai/cxr-suite/",
"categories": [
"diagnostic",
"clinical",
"radiology",
"medical imaging",
"quality assurance",
"workflow automation",
"AI second reader"
],
"market_segment": [
"enterprise",
"hospital",
"health system",
"smb"
],
"links": [
"https://oxipit.ai/",
"https://oxipit.ai/cxr-suite/",
"https://oxipit.ai/regulatory-compliance-and-certifications/",
"https://oxipit.ai/wp-content/uploads/2024/04/2.1.-Instruction-for-use-User-Manual-EN-ChestEye.pdf",
"https://alma-medical.com/en/marketplaces/chesteye-quality/",
"https://healthairegister.com/products/oxipit-chesteye-quality",
"https://blackfordanalysis.com/ai-portfolio-oxipit-chesteye-quality",
"https://oxipit.ai/news/oxipit-quality/",
"https://www.nature.com/articles/s41598-024-55792-1"
],
"summary": "AI-driven quality assurance tool that automatically double-reads chest X-rays by comparing radiologist reports with AI image analysis to flag potential missed findings.",
"description": "ChestEye Quality (Oxipit) is an automatic QA solution for chest radiography that analyzes final radiologist reports alongside chest X-ray images using AI to identify potential false negatives, flagging cases for secondary human review and delivering alerts via PACS/RIS/HIS, email or structured messages (DICOM SR/HL7). It supports prospective and retrospective auditing, integrates into standard reading workflows, and is deployable cloud or on-premises.",
"target_users": [
"radiologists",
"radiology departments",
"radiology QA managers",
"hospital administrators",
"clinical governance teams"
],
"specialties": [
"chest radiology",
"thoracic imaging",
"pulmonology"
],
"regions_available": [
"Europe (CE-marked)",
"Brazil"
],
"languages_supported": [],
"pricing_model": "subscription",
"pricing_details": "Subscription; pricing based on number of analyses and number of installations; contact vendor for quotes and trial information",
"license": "commercial/proprietary",
"features": [
"automated CXR double-reading (quality assurance)",
"preliminary AI-generated report for common findings",
"heatmaps/visualization of detected findings",
"priority/triage flagging",
"comparison of radiologist report vs image to detect missed findings",
"retrospective similar-case search",
"case queue/management",
"analytics/dashboard for QA metrics",
"support for multilingual reporting",
"integration with PACS/RIS workflows"
],
"integrations": [
"PACS",
"RIS",
"Sectra",
"Alma Medical (partner/integration)",
"Blackford (marketplace/integration)"
],
"deployment_model": [
"on_prem",
"cloud",
"hybrid",
"SaaS"
],
"os_platforms": [
"web"
],
"optional_modules": [
"API interface",
"analytics module",
"web user interface"
],
"data_standards": [
"DICOM",
"HL7 v2"
],
"api_available": "yes",
"system_requirements": "",
"compliance": [
"CE (medical device)"
],
"certifications": [
"CE mark",
"ANVISA (Brazil) clearance"
],
"security_features": [],
"privacy_features": [],
"data_residency": "",
"customers": [
"Šeškinės Poliklinika",
"FIDI (Fundação Instituto de Pesquisa e Estudo de Diagnóstico por Imagem)",
"Unilabs",
"Vestre Viken Health Trust",
"Leiden University Medical Centre (LUMC)"
],
"user_reviews": [
"It flags discrepancies for radiologists' review, aiming to improve reporting accuracy and act as a preventive measure against diagnostic errors.",
"Uses AI as a 'second reader' — analyzes images and corresponding reports to spot missed findings.",
"Real-world testing showed the tool identified clinically relevant missed findings and acted as a safety net for radiologists."
],
"ratings": [],
"support_channels": [
"email",
"phone",
"ticketing"
],
"training_options": [
"documentation",
"webinars",
"live_online"
],
"release_year": "2021",
"integration_partners": [
"Alma Health Platform",
"Blackford Analysis",
"CARPL.AI",
"Deepc",
"Sectra",
"contextflow",
"Unilabs",
"Romion Health"
],
"id": "P2075",
"slug": "chesteye-quality",
"type": "product",
"version": "1.0",
"last_updated": "2025-09-07",
"links_json": {
"self": "https://www.healthaidb.com/software/chesteye-quality.json"
}
}