Care Coordination
AI-powered care coordination for timely stroke intervention
Platform · Cloud SaaSCompany source
Company profile
About Qure.ai
Qure.ai uses deep learning to make healthcare more equitable for patients worldwide, powering efficient identification and management of diseases including lung cancer and tuberculosis.
Company description Qure.ai official website
Primary marketMedical Imaging & Radiology
Commercial modelCommercial information available on request
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01 · Operating footprint
Where the Company operates
Company-profile context for clinical focus, customers, deployment, integration, milestones, and partnerships.
Clinical focus
- Heart Failure
- Lung Cancer
- Lung Nodules
- Pediatric TB
- Silicosis
- Stroke
- Traumatic Brain Injury
- Tuberculosis
Who it serves
- Country Programmes
- Health Systems
- Hospitals
- Imaging Centers
- Immigration Authorities
- Ministries of Health
- Pharma Companies
- Public & Private Care Providers
- Tele-radiology Services
Deployment & integration
Cloud Saas · Hybrid deployment with recorded integration context.
Recorded integration context: Compatible with all major CR, DR and ultraportable X-ray systems.
Geographic context
MarketsGlobal presence in 105+ countries · India · Lesotho · Mozambique · Philippines · Scotland · UK · USA
Company sourceOfficial website
Company milestones
Platform expansion
Recognized among TIME's 100 Most Influential Companies of 2025. Partnership with VillageReach to strengthen Mozambique's AI-enabled pandemic response (May 2026). AstraZeneca partnership targeting 5 million patient screenings by 2025 as part of EDISON Alliance's 1 Billion Lives Challenge. 8,700 TB cases diagnosed through ACCESS TB project in Philippines.
Partnerships
Recorded partners
- Aarthi Scans
- AstraZeneca
- Medica Group
- Medtronic India
- Merck Global Health Innovation Fund
- NHS Greater Glasgow and Clyde
- Nottingham University Hospitals NHS Trust
- Project Data Sphere
- Save The Children US
- University of Glasgow
- VillageReach
02 · Flagship portfolio
Products, workflows, and platform capabilities
Selected source-backed Company products and the available commercial context.
qCT
AI for early detection and monitoring of lung nodules on CT scans
Software · HybridCompany source
qCT LN Quant
AI-powered chest CT solution for advanced lung nodule quantification and tracking
Software · Cloud SaaSCompany source
qER
AI solution for triaging emergency neurocritical findings
Software · Cloud SaaSCompany source
qLC-Suite (Lung Nodule Management)
AI for early detection and management of lung cancer
Platform · HybridCompany source
qTrack
AI Powered TB care cascades - WHO evaluated AI-based TB screening and triage tool
Platform · HybridCompany source
Qure AI App
Mobile and web application for AI-powered medical imaging analysis
Software · Cloud SaaSCompany source
qXR
AI-enabled comprehensive Chest X-ray reporting tool
Software · HybridCompany source
qXR LN
AI-powered early detection and localization of lung nodules on chest X-rays
Software · Cloud SaaSCompany source
03 · Research intelligence
Research Intelligence evidence
Publications, preprints, clinical validation, and regulatory records in Evidence Position order.
Evidence position
Publications, clinical validation, and regulatory records connected to this Company appear here.
Research
Publications & preprints
Budget impact of implementing AI-enabled chest X-ray based incidental pulmonary nodule detection for early lung cancer diagnosis: models from Colombia, Costa Rica, Mexico, Thailand and Vietnam.Journal of medical economics · Dec 1, 2026
Beyond Validation: Operationalising Post-Deployment Surveillance of AI Medical Devices in Clinical Practice.Journal of medical systems · Jun 19, 2026
Evaluation of an artificial intelligence generated Brock score to determine malignancy risk of screen-detected pulmonary nodules.European journal of radiology open · Jun 1, 2026
Comparison of Lung Cancer Screening Programs in Liver Transplant Recipients and the General Population.Archivos de bronconeumologia · Apr 9, 2026
Accelerating tuberculosis diagnosis in Mozambican prisons using digital chest X-rays with computer-aided detection: a longitudinal, comprehensive health intervention.BMC global and public health · Apr 3, 2026
Evaluation of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Incidental Pulmonary Nodules: The CREATE Study.Mayo Clinic proceedings. Digital health · Mar 1, 2026
Clinical validation
Clinical-trial records
No linked records are currently available.
Regulatory
Regulatory records
qXR-DetectK251934 · 510k · Jan 16, 2026
qER-CTA (v1.0)K251610 · 510k · Sep 8, 2025
qCT LN QuantK240740 · 510k · Aug 16, 2024
qXR-LNK231805 · 510k · Dec 22, 2023
qXR-CTRK231149 · 510k · Sep 22, 2023
qXR-PTX-PEK230899 · 510k · Aug 22, 2023
04 · Company-reported outcomes
Outcomes and case studies
Company-reported outcomes and customer stories are distinct from linked Research Intelligence evidence.
40% TAT reduction
Reporting turnaround time by classifying 40% normal X-rays with 98.9% NPV
25% additional TB cases detected
Via incidental pathway
99% reduction in TAT to confirmed diagnosis
From 3 weeks to 2 hours for TB
50% cost savings
On confirmatory tests for TB
$200 cost saved
Per notified TB case
46% faster reporting
For nodules with qCT
40% fewer nodules missed
With qXR
9/10 missed nodules identified
By radiologists using qXR
99% NPV
In segregating unremarkable CXRs
VillageReach and Mozambique Health System
AI-enabled pandemic preparedness and disease surveillance in Mozambique
Strengthened AI-enabled pandemic preparedness capabilities
AstraZeneca
Long-term partnership applying AI to routine chest X-rays for earlier lung cancer risk identification across 20 countries
Five million scan milestone achieved across 20 countries
ACCESS TB Project, Philippines
Community-based TB screening using qTrack platform
8,700 TB cases diagnosed and notified through community-based screening
Aarthi Scans (India)
Tele-radiology growth through AI-powered chest X-ray reporting
40% TAT reduction by classifying 40% normal X-rays with 98.9% NPV
AstraZeneca (EDISON Alliance 1 Billion Lives Challenge)
Large-scale patient screening for early disease detection in underserved communities
Screening 5 million patients by 2025 as part of 1 Billion Lives Challenge
AstraZeneca (EDISON Alliance)
Large-scale patient screening for early disease detection
Screening 5 million patients by 2025 as part of 1 Billion Lives Challenge
Comprehensive Cancer Centers of Nevada
Early-stage lung cancer detection through incidental nodule identification
Significant role in early detection of incidental nodules in pulmonary imaging
Massachusetts General Hospital
Multi-center publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions
Up to 96% sensitivity and 100% specificity for the qXR algorithm
Medica Group
Integration of augmented intelligence into healthcare to enhance diagnostic accuracy
Improved patient outcomes through cutting-edge AI tools supporting clinicians
Medtronic India
Stroke diagnosis and treatment support through AI-enabled decision-making and data sharing between hospitals
Improved efficiency enabling more stroke patients to be treated within critical time window
Ministry of Health, Lesotho
TB screening and case management using AI integrated with existing X-ray systems
Prompt screening with immediate results and timely referral for TB case management
NHS Greater Glasgow and Clyde (NHSGGC)
Early detection of lung cancer through AI-enhanced chest X-ray screening to improve time to further imaging and treatment
Processing 100,000 chest X-rays annually with improved speed and accuracy for early cancer detection
Nottingham University Hospitals NHS Trust
Improving pathway logistics by flagging abnormalities on chest X-rays to help patients progress rapidly to CT scanning
Improved patient progression to CT scanning through automated flagging of abnormalities
Sinai Chicago
Real lung nodule workflows using qXR and qCT for detection and annotation with collaboration between radiologists and referring physicians
qXR's ability to detect subtle nodules without creating reader fatigue; refined annotation tools supporting both radiologists and referring physicians
University Hospitals Cleveland Medical Center
Clinical trial evaluating AI-driven approach for lung nodule detection and early lung cancer diagnosis
Clinical trial evaluating how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI
vRad
Validation and deployment of qER solution for intracranial hemorrhage detection to improve emergency care for patients
Extensive validation completed with partnership continuing to improve patient care
05 · Leadership
Leadership team
Leadership profiles and professional links from the current Company record.
06 · Company updates
Updates from Qure.ai
Recent articles published on Qure.ai's official website.
07 · HAIC coverage
Coverage and analysis
Health AI Central reporting that mentions this Company.
No HAIC coverage is currently linked to this Company.
08 · Official presence
Company sources and channels
Curated official destinations from the active Company record.
09 · Market pathway
Additional Companies in this Market
Other active Companies in the same market, selected alphabetically from the HAIC directory.
Market context
Medical Imaging & Radiology
Companies that apply AI to analyze medical images for diagnostic purposes, workflow optimization, or clinical decision support. Covers general radiology (X-rays, CT scans, MRIs, ultrasounds) across multiple organ systems, as well as specialized imaging in ophthalmology and dermatology.
Explore the full market directory or compare this Company with additional active profiles.
View all Companies in this market
10 · Pro research
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Expanded Company research is in development.
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Pro Research will introduce expanded technical context, linked evidence analysis, and comparative Company research.
Linked research, clinical, and regulatory source pathways.
Expanded product, deployment, integration, and partnership research.
Record-level provenance and review context.