From Image
to Intervention
FDA-cleared lung cancer AI that flags nodules, stratifies malignancy risk on CT, and closes the loop on follow-up – trusted and deployed worldwide with leading health systems and across global care networks.
For conditions such as intracranial hemorrhage, time is of the essence and those precious minutes can be life-changing for our patients. We have done extensive validation of the Qure.ai qER solution and are excited to continue to partner with Qure.ai and improve care for our patients.
Benjamin W. Strong
MD and Chief Medical Officer
vRad
A multicenter publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions reported up to 96% sensitivity and 100% specificity for the qXR algorithm.
Dr Subba Digumarthy, MD
Attending Radiologist, Thoracic Imaging, Massachusetts General Hospital
Associate Professor, Harvard Medical School
As an Emergency Medicine Physician, I have 4 to 5 patients undergoing various imaging studies at any time. The AI's ability to rapidly triage pneumothorax dramatically improves my speed and efficiency by alerting me to critical pathology far before a radiologist, or I personally have time to review the film.
Neil Roy
MD, MBA, FACEP, CPE; Chief Medical Officer
Shady Grove Medical Center
Medical imaging AI holds immense potential in the battle against lung cancer in the United States. It is great to see the breadth of FDA clearances rolling in to enable the exploration and activation of algorithms that can support radiologists and pulmonologists. This will help to detect lung nodules earlier using chest X-ray, and also analyze them in detail on chest CT.
Dr. Javier Zulueta
MD, Chief, Division of Pulmonary, Critical Care and Sleep Medicine at Mount Sinai Morningside, New York
AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation. This AI-driven approach may aid in identifying more nodules which we hope supports patient care and enables us to evaluate the broader impact of medical imaging AI. The clinical trial will evaluate how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI. The hope is that this clinical trial will not only advance early detection but also drive meaningful transformation in lung cancer surveillance
Amit Gupta
Cardiothoracic Radiologist and Modality Director of Diagnostic Radiography at University Hospitals Cleveland Medical Center
I’ve had the opportunity to work extensively with qXR and qCT in real lung nodule workflows through our Sinai Chicago collaboration with Qure.ai. qXR’s ability to detect subtle nodules without creating reader fatigue has been particularly impactful. And on the qCT side, collaborating directly with Qure’s product engineers to refine the annotation tools has resulted in a workflow that truly supports both radiologists and referring physicians. It’s rewarding to contribute to a solution designed with the end user and our patients in mind.
Dr Amar Shah, MD
Chairman, Mount Sinai Hospital, Chicago
For conditions such as intracranial hemorrhage, time is of the essence and those precious minutes can be life-changing for our patients. We have done extensive validation of the Qure.ai qER solution and are excited to continue to partner with Qure.ai and improve care for our patients.
Benjamin W. Strong
MD and Chief Medical Officer
vRad
A multicenter publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions reported up to 96% sensitivity and 100% specificity for the qXR algorithm.
Dr Subba Digumarthy, MD
Attending Radiologist, Thoracic Imaging, Massachusetts General Hospital
Associate Professor, Harvard Medical School
As an Emergency Medicine Physician, I have 4 to 5 patients undergoing various imaging studies at any time. The AI's ability to rapidly triage pneumothorax dramatically improves my speed and efficiency by alerting me to critical pathology far before a radiologist, or I personally have time to review the film.
Neil Roy
MD, MBA, FACEP, CPE; Chief Medical Officer
Shady Grove Medical Center
Medical imaging AI holds immense potential in the battle against lung cancer in the United States. It is great to see the breadth of FDA clearances rolling in to enable the exploration and activation of algorithms that can support radiologists and pulmonologists. This will help to detect lung nodules earlier using chest X-ray, and also analyze them in detail on chest CT.
Dr. Javier Zulueta
MD, Chief, Division of Pulmonary, Critical Care and Sleep Medicine at Mount Sinai Morningside, New York
AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation. This AI-driven approach may aid in identifying more nodules which we hope supports patient care and enables us to evaluate the broader impact of medical imaging AI. The clinical trial will evaluate how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI. The hope is that this clinical trial will not only advance early detection but also drive meaningful transformation in lung cancer surveillance
Amit Gupta
Cardiothoracic Radiologist and Modality Director of Diagnostic Radiography at University Hospitals Cleveland Medical Center
I’ve had the opportunity to work extensively with qXR and qCT in real lung nodule workflows through our Sinai Chicago collaboration with Qure.ai. qXR’s ability to detect subtle nodules without creating reader fatigue has been particularly impactful. And on the qCT side, collaborating directly with Qure’s product engineers to refine the annotation tools has resulted in a workflow that truly supports both radiologists and referring physicians. It’s rewarding to contribute to a solution designed with the end user and our patients in mind.
Dr Amar Shah, MD
Chairman, Mount Sinai Hospital, Chicago
For conditions such as intracranial hemorrhage, time is of the essence and those precious minutes can be life-changing for our patients. We have done extensive validation of the Qure.ai qER solution and are excited to continue to partner with Qure.ai and improve care for our patients.
Benjamin W. Strong
MD and Chief Medical Officer
vRad
A multicenter publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions reported up to 96% sensitivity and 100% specificity for the qXR algorithm.
Dr Subba Digumarthy, MD
Attending Radiologist, Thoracic Imaging, Massachusetts General Hospital
Associate Professor, Harvard Medical School
As an Emergency Medicine Physician, I have 4 to 5 patients undergoing various imaging studies at any time. The AI's ability to rapidly triage pneumothorax dramatically improves my speed and efficiency by alerting me to critical pathology far before a radiologist, or I personally have time to review the film.
Neil Roy
MD, MBA, FACEP, CPE; Chief Medical Officer
Shady Grove Medical Center
Medical imaging AI holds immense potential in the battle against lung cancer in the United States. It is great to see the breadth of FDA clearances rolling in to enable the exploration and activation of algorithms that can support radiologists and pulmonologists. This will help to detect lung nodules earlier using chest X-ray, and also analyze them in detail on chest CT.
Dr. Javier Zulueta
MD, Chief, Division of Pulmonary, Critical Care and Sleep Medicine at Mount Sinai Morningside, New York
AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation. This AI-driven approach may aid in identifying more nodules which we hope supports patient care and enables us to evaluate the broader impact of medical imaging AI. The clinical trial will evaluate how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI. The hope is that this clinical trial will not only advance early detection but also drive meaningful transformation in lung cancer surveillance
Amit Gupta
Cardiothoracic Radiologist and Modality Director of Diagnostic Radiography at University Hospitals Cleveland Medical Center
I’ve had the opportunity to work extensively with qXR and qCT in real lung nodule workflows through our Sinai Chicago collaboration with Qure.ai. qXR’s ability to detect subtle nodules without creating reader fatigue has been particularly impactful. And on the qCT side, collaborating directly with Qure’s product engineers to refine the annotation tools has resulted in a workflow that truly supports both radiologists and referring physicians. It’s rewarding to contribute to a solution designed with the end user and our patients in mind.
Dr Amar Shah, MD
Chairman, Mount Sinai Hospital, Chicago
For conditions such as intracranial hemorrhage, time is of the essence and those precious minutes can be life-changing for our patients. We have done extensive validation of the Qure.ai qER solution and are excited to continue to partner with Qure.ai and improve care for our patients.
Benjamin W. Strong
MD and Chief Medical Officer
vRad
A multicenter publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions reported up to 96% sensitivity and 100% specificity for the qXR algorithm.
Dr Subba Digumarthy, MD
Attending Radiologist, Thoracic Imaging, Massachusetts General Hospital
Associate Professor, Harvard Medical School
As an Emergency Medicine Physician, I have 4 to 5 patients undergoing various imaging studies at any time. The AI's ability to rapidly triage pneumothorax dramatically improves my speed and efficiency by alerting me to critical pathology far before a radiologist, or I personally have time to review the film.
Neil Roy
MD, MBA, FACEP, CPE; Chief Medical Officer
Shady Grove Medical Center
Medical imaging AI holds immense potential in the battle against lung cancer in the United States. It is great to see the breadth of FDA clearances rolling in to enable the exploration and activation of algorithms that can support radiologists and pulmonologists. This will help to detect lung nodules earlier using chest X-ray, and also analyze them in detail on chest CT.
Dr. Javier Zulueta
MD, Chief, Division of Pulmonary, Critical Care and Sleep Medicine at Mount Sinai Morningside, New York
AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation. This AI-driven approach may aid in identifying more nodules which we hope supports patient care and enables us to evaluate the broader impact of medical imaging AI. The clinical trial will evaluate how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI. The hope is that this clinical trial will not only advance early detection but also drive meaningful transformation in lung cancer surveillance
Amit Gupta
Cardiothoracic Radiologist and Modality Director of Diagnostic Radiography at University Hospitals Cleveland Medical Center
I’ve had the opportunity to work extensively with qXR and qCT in real lung nodule workflows through our Sinai Chicago collaboration with Qure.ai. qXR’s ability to detect subtle nodules without creating reader fatigue has been particularly impactful. And on the qCT side, collaborating directly with Qure’s product engineers to refine the annotation tools has resulted in a workflow that truly supports both radiologists and referring physicians. It’s rewarding to contribute to a solution designed with the end user and our patients in mind.
Dr Amar Shah, MD
Chairman, Mount Sinai Hospital, Chicago
For conditions such as intracranial hemorrhage, time is of the essence and those precious minutes can be life-changing for our patients. We have done extensive validation of the Qure.ai qER solution and are excited to continue to partner with Qure.ai and improve care for our patients.
Benjamin W. Strong
MD and Chief Medical Officer
vRad
A multicenter publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions reported up to 96% sensitivity and 100% specificity for the qXR algorithm.
Dr Subba Digumarthy, MD
Attending Radiologist, Thoracic Imaging, Massachusetts General Hospital
Associate Professor, Harvard Medical School
As an Emergency Medicine Physician, I have 4 to 5 patients undergoing various imaging studies at any time. The AI's ability to rapidly triage pneumothorax dramatically improves my speed and efficiency by alerting me to critical pathology far before a radiologist, or I personally have time to review the film.
Neil Roy
MD, MBA, FACEP, CPE; Chief Medical Officer
Shady Grove Medical Center
Medical imaging AI holds immense potential in the battle against lung cancer in the United States. It is great to see the breadth of FDA clearances rolling in to enable the exploration and activation of algorithms that can support radiologists and pulmonologists. This will help to detect lung nodules earlier using chest X-ray, and also analyze them in detail on chest CT.
Dr. Javier Zulueta
MD, Chief, Division of Pulmonary, Critical Care and Sleep Medicine at Mount Sinai Morningside, New York
AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation. This AI-driven approach may aid in identifying more nodules which we hope supports patient care and enables us to evaluate the broader impact of medical imaging AI. The clinical trial will evaluate how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI. The hope is that this clinical trial will not only advance early detection but also drive meaningful transformation in lung cancer surveillance
Amit Gupta
Cardiothoracic Radiologist and Modality Director of Diagnostic Radiography at University Hospitals Cleveland Medical Center
I’ve had the opportunity to work extensively with qXR and qCT in real lung nodule workflows through our Sinai Chicago collaboration with Qure.ai. qXR’s ability to detect subtle nodules without creating reader fatigue has been particularly impactful. And on the qCT side, collaborating directly with Qure’s product engineers to refine the annotation tools has resulted in a workflow that truly supports both radiologists and referring physicians. It’s rewarding to contribute to a solution designed with the end user and our patients in mind.
Dr Amar Shah, MD
Chairman, Mount Sinai Hospital, Chicago
For conditions such as intracranial hemorrhage, time is of the essence and those precious minutes can be life-changing for our patients. We have done extensive validation of the Qure.ai qER solution and are excited to continue to partner with Qure.ai and improve care for our patients.
Benjamin W. Strong
MD and Chief Medical Officer
vRad
A multicenter publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions reported up to 96% sensitivity and 100% specificity for the qXR algorithm.
Dr Subba Digumarthy, MD
Attending Radiologist, Thoracic Imaging, Massachusetts General Hospital
Associate Professor, Harvard Medical School
As an Emergency Medicine Physician, I have 4 to 5 patients undergoing various imaging studies at any time. The AI's ability to rapidly triage pneumothorax dramatically improves my speed and efficiency by alerting me to critical pathology far before a radiologist, or I personally have time to review the film.
Neil Roy
MD, MBA, FACEP, CPE; Chief Medical Officer
Shady Grove Medical Center
Medical imaging AI holds immense potential in the battle against lung cancer in the United States. It is great to see the breadth of FDA clearances rolling in to enable the exploration and activation of algorithms that can support radiologists and pulmonologists. This will help to detect lung nodules earlier using chest X-ray, and also analyze them in detail on chest CT.
Dr. Javier Zulueta
MD, Chief, Division of Pulmonary, Critical Care and Sleep Medicine at Mount Sinai Morningside, New York
AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation. This AI-driven approach may aid in identifying more nodules which we hope supports patient care and enables us to evaluate the broader impact of medical imaging AI. The clinical trial will evaluate how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI. The hope is that this clinical trial will not only advance early detection but also drive meaningful transformation in lung cancer surveillance
Amit Gupta
Cardiothoracic Radiologist and Modality Director of Diagnostic Radiography at University Hospitals Cleveland Medical Center
I’ve had the opportunity to work extensively with qXR and qCT in real lung nodule workflows through our Sinai Chicago collaboration with Qure.ai. qXR’s ability to detect subtle nodules without creating reader fatigue has been particularly impactful. And on the qCT side, collaborating directly with Qure’s product engineers to refine the annotation tools has resulted in a workflow that truly supports both radiologists and referring physicians. It’s rewarding to contribute to a solution designed with the end user and our patients in mind.
Dr Amar Shah, MD
Chairman, Mount Sinai Hospital, Chicago
For conditions such as intracranial hemorrhage, time is of the essence and those precious minutes can be life-changing for our patients. We have done extensive validation of the Qure.ai qER solution and are excited to continue to partner with Qure.ai and improve care for our patients.
Benjamin W. Strong
MD and Chief Medical Officer
vRad
A multicenter publication on missed and mislabeled chest radiography findings including pneumothoraces and pleural effusions reported up to 96% sensitivity and 100% specificity for the qXR algorithm.
Dr Subba Digumarthy, MD
Attending Radiologist, Thoracic Imaging, Massachusetts General Hospital
Associate Professor, Harvard Medical School
As an Emergency Medicine Physician, I have 4 to 5 patients undergoing various imaging studies at any time. The AI's ability to rapidly triage pneumothorax dramatically improves my speed and efficiency by alerting me to critical pathology far before a radiologist, or I personally have time to review the film.
Neil Roy
MD, MBA, FACEP, CPE; Chief Medical Officer
Shady Grove Medical Center
Medical imaging AI holds immense potential in the battle against lung cancer in the United States. It is great to see the breadth of FDA clearances rolling in to enable the exploration and activation of algorithms that can support radiologists and pulmonologists. This will help to detect lung nodules earlier using chest X-ray, and also analyze them in detail on chest CT.
Dr. Javier Zulueta
MD, Chief, Division of Pulmonary, Critical Care and Sleep Medicine at Mount Sinai Morningside, New York
AI serves as an additional set of eyes for radiologists, enhancing detection by flagging lung nodules that may require further evaluation. This AI-driven approach may aid in identifying more nodules which we hope supports patient care and enables us to evaluate the broader impact of medical imaging AI. The clinical trial will evaluate how many patients require follow-up CT scans, biopsies, and how many more lung cancer cases are diagnosed earlier using AI. The hope is that this clinical trial will not only advance early detection but also drive meaningful transformation in lung cancer surveillance
Amit Gupta
Cardiothoracic Radiologist and Modality Director of Diagnostic Radiography at University Hospitals Cleveland Medical Center
I’ve had the opportunity to work extensively with qXR and qCT in real lung nodule workflows through our Sinai Chicago collaboration with Qure.ai. qXR’s ability to detect subtle nodules without creating reader fatigue has been particularly impactful. And on the qCT side, collaborating directly with Qure’s product engineers to refine the annotation tools has resulted in a workflow that truly supports both radiologists and referring physicians. It’s rewarding to contribute to a solution designed with the end user and our patients in mind.
Dr Amar Shah, MD
Chairman, Mount Sinai Hospital, Chicago
The only platform unifying detection and follow-up
Build detection and navigation in a single workflow. Start where you are, upgrade anytime.
A Connected Pathway to Early
Detection, Intervention & Care
Qure’s comprehensive FDA-cleared reimbursable solutions that help health systems detect early and follow through automatically. The solutions support radiologists, nurse navigators, interventional pulmonologists, and thoracic surgeons with a single unified platform.
Highlight Risk on CT (qCT)
- Detect and characterize nodules through AI
- Specialist-ready nodule measurements
- Support pulmonologists with reimbursable quantification solutions
Drive Care Forward with qTrack
- Automate longitudinal tracking through AI
- Spend time verifying, not entering data
- Collate clinical details via EMR integrations
Identify Lung Nodules on Chest Radiographs (qXR)
- Automate identification of subtle pulmonary nodules
- Catch more at-risk patients
Seamless Integrations and Reporting
Built to fit existing systems and speed interpretation without changing how you work.
Temporal comparisons
- Contextualize by comparing priors
- Assess risk via automating interval change
FDA-cleared automated volumetry
- Objective growth assessment on CTs
- Consistent measurements to save time
Dictation integrations
- Seamlessly integrate with existing dictation systems
- Reporting faster and easier
- Customise to various PACS, local reporting guidelines
Individual Solutions
qTrack
qTrack: Follow-up & Navigation
- Real-time patient tracking, adherence dashboards, enrollment automation
- Anchors incidental nodule intake, patient communication, automated recalls (follow-up management)
- Integrated DICOM Viewer for imaging review ..
qCT
qCT: Risk & Quantification
- Specialist-ready nodule quantification and malignancy risk stratification
- Supports prioritization and surgical decision-making
- Customizable reports aligned to tumor boards or hospital workflows
qXR
qXR: CXR Intake
- CXR-based pulmonary nodule flagging to capture incidentals earlier
- Configurable routing to qTrack for timely recalls and follow-up
- Fits existing imaging/PACS workflows and downstream CT evaluation
Atlas
Atlas: Tumor Board Automation
- Automated multidisciplinary tumor board workflows and exports
- Standardized case packets to streamline review and decisions
Aira
Aira Insights (Program Analytics)
- Real-time throughput and adherence dashboards across your IPN/LNP
- Cohort views and time-interval tracking across pathway steps
- Exportable summaries for leadership; optional modeled value views
qER
qER (Emergency Suite)
- Triage support for emergent findings on CT/X-ray (e.g., ICH, LVO, PE, PTX, fractures)
- Designed to aid rapid prioritization across ED workflows
The advantages of LLM-based AI solutions
NLP
Rule-first Processing
Treats all nodules the same without anatomy, risk, or context.
Relies on predefined rules & keywords lists
Finds the word, not the meaning
Scans reports for exact matches
Triggers in any occurrence of “nodule”
Ignores critical context
- Synonyms & reporting variations confuse rules
- All nodules treated the same
Leads to poor outcomes
- 30–40% overcalling → alert fatigue
- 10–20% undercalling → misses
LLM
Reason-first Intelligence
Extracts context to assess true risk per nodule.
Integrates multiple data sources
Prior imaging, history, risk factor, report text
Understands full clinical context
Analyzes findings in relation to the specific patient
Reasoning like a trained junior doctor
- Handles synonyms, nuance, report styles
- Produces logic-based clinical insights
Delivers intelligent outcomes
- Accurate triage & risk assessment
- < 5% error rate with minimal false positive
Reimbursement
Reimbursement for AI-enabled lung cancer and neurocritical care pathways is evolving. Qure’s products within the lung cancer and neuro pathways are billable under CMS-issued CPT codes (such as 0721T, 0727T), allowing hospitals to establish a stable revenue model while optimally utilizing AI solutions to improve accurate and faster diagnostics. Qure works with revenue-cycle teams to align documentation, coding, and payer engagement within existing imaging and follow-up workflows. Some partners report success under established routes; others are assessing emerging options as coverage varies by payer type. Please consult local medical-necessity and documentation policies before billing and prior authorization.
For more information please write to us at partner@qure.ai
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