Incidental Findingto Intentional Care.
Smarter pulmonary pathways, powered by AI
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Ten patients walk in. One completes care.
For every referral made, nine more fall through the cracks.
The finding that never became a referral.
Incidental and hard-to-spot pulmonary findings can sit buried in routine imaging.
- 65% referrals do not complete appointments as per follow-up. 1
- 1.1M of the 1.6M US lung nodules written into a report every year never get timely follow-up. 2
Screening finds a fraction. Routine imaging & medical records finds the rest.
A referral isn't care until the patient gets there.
The next step might be a clinic visit, interval CT, PET or biopsy. Manual outreach breaks at scale, turning care coordination into a bottleneck
- 3-5% of findings are missed on every modality as exams are read. 3
- 19% Incidental findings are reported but not surfaced. 4
Finding the patient is the first half. Moving them to care is the other.
One challenge limits who enters care. The other limits whether they receive timely intervention. Together, they leave patients behind.
Every clinically significant finding deserves the opportunity to become timely care.
See how Qure.ai helps health systems connect the pulmonary care pathway from earlier identification to intervention.
Trusted by healthcare systems, proven by practice.
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
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
Dr. 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 P. Shah
MD - System Chair of Radiology, Sinai Chicago, Chicago IL
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
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
Dr. 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 P. Shah
MD - System Chair of Radiology, Sinai Chicago, Chicago IL
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
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
Dr. 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 P. Shah
MD - System Chair of Radiology, Sinai Chicago, Chicago IL
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
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
Dr. 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 P. Shah
MD - System Chair of Radiology, Sinai Chicago, Chicago IL
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
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
Dr. 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 P. Shah
MD - System Chair of Radiology, Sinai Chicago, Chicago IL
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
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
Dr. 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 P. Shah
MD - System Chair of Radiology, Sinai Chicago, Chicago IL
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
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
Dr. 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 P. Shah
MD - System Chair of Radiology, Sinai Chicago, Chicago IL
Shaping the future of pulmonary health with AI
