AI biomarkers:From clinical trialsto real-worldimpact, at scale.
Qure.ai is enabling a paradigm shift in life sciences from reactive care to proactive, data-driven patient identification and management. By harnessing AI-powered clinical intelligence, we translate healthcare data into actionable insights that enable earlier intervention, stage shift & faster access to therapies in a coordinated care pathway.
Translating to equitable healthcare.
AI-derived biomarkers help identify patients at risk of serious disease earlier in their journey, supporting earlier diagnosis and entry into treatment pathways.
Agentic AI capabilities help identify and surface potential patient cohorts, enabling life sciences teams to explore faster clinical trial recruitment and program readiness.
Deployed across 5,200+ active sites in 105 countries, Qure.ai enables scalable AI adoption across diverse healthcare systems and care environments.
With 26 FDA-cleared findings, Qure.ai supports consistent detection and structured insights that help align care pathways with clinical guidelines.
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
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
Ex 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
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
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
Ex 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
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
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
Ex 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
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
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
Ex 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
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
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
Ex 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
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
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
Ex 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
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
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
Ex 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
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
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