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We are regularly featured in the latest scientific journals. See below for more of our impact.
2023
2022
2021
- Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports
- Lumbar MRI Reporting Efficiency for Trainees Over the Academic Year: An Opportunity for Improving Clinical Workflows in Academic Medical Centers
- The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
- The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications
2020
- Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves
- Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents
2023
- Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia
- Detection of idiopathic normal pressure hydrocephalus on head CT using a deep convolutional neural network
- Head CT deep learning model is highly accurate for early infarct estimation
2022
- Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT
- Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
- Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas
- Correlating malignancy risk from an artificial intelligence (AI) algorithm and lung-rads-based classification from screening low-dose CT imaging
- Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging
- Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT
- Predictive values of AI-based triage model in suboptimal CT pulmonary angiography
- Predictive values of AI-based triage model in suboptimal CT pulmonary angiography
- Successful creation of clinical AI without data scientists or software developers: radiologist-trained AI model for identifying suboptimal chest-radiographs
- Use of Artificial Intelligence in Clinical Neurology
2021
- A Deep Learning–based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience
- Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
- Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas
- Data Management in Artificial Intelligence–Assisted Radiology Reporting
- Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19
- Federated learning for predicting clinical outcomes in patients with COVID-19
- Lumbar MRI Reporting Efficiency for Trainees Over the Academic Year: An Opportunity for Improving Clinical Workflows in Academic Medical Centers
- Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
- Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study
- The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
- Use of Machine Learning Algorithms to Determine Features of Systolic Blood Pressure Variability that Predict Poor Outcomes in Hypertensive Patients
2020
- CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia
- Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19
- Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels
2018
- Deep Spine: Automated Lumbar Vertebral Segmentation, Disc-level Designation and Spinal Stenosis Grading Using Deep Learning
- Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
2017
2023
- Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia
- Detection of idiopathic normal pressure hydrocephalus on head CT using a deep convolutional neural network
- Head CT deep learning model is highly accurate for early infarct estimation
2022
- Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
- Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas
- Correlating malignancy risk from an artificial intelligence (AI) algorithm and lung-rads-based classification from screening low-dose CT imaging
- Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
- FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Large-Scale Model Validation in Healthcare
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT
- Predictive values of AI-based triage model in suboptimal CT pulmonary angiography
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
- Successful creation of clinical AI without data scientists or software developers: radiologist-trained AI model for identifying suboptimal chest-radiographs
- Validation pipeline for machine learning algorithm assessment for multiple vendors
2021
- A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records
- Artificial Intelligence Enabling Radiology Reporting
- Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT
- Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
- Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19
- Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues
- Lessons Learned From the Front Lines of Artificial Intelligence Implementation
- Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
- Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study
2020
- CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia
- Federated Learning for Breast Density Classification: A Real-World Implementation
- Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19
- Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels
2022
2021
- Lumbar MRI Reporting Efficiency for Trainees Over the Academic Year: An Opportunity for Improving Clinical Workflows in Academic Medical Centers
- The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
2020
2018
2023
- Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia
- Detection of idiopathic normal pressure hydrocephalus on head CT using a deep convolutional neural network
- Head CT deep learning model is highly accurate for early infarct estimation
2022
- A Fully Automated Deep Learning Pipeline for Multi–Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans
- Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model
- Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT
- Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
- Complex Relationship Between Artificial Intelligence and CT Radiation Dose
- Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas
- Correlating malignancy risk from an artificial intelligence (AI) algorithm and lung-rads-based classification from screening low-dose CT imaging
- Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging
- Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Imaging Information Overload: Quantifying the Burden of Interpretive and Non-Interpretive Tasks for Computed Tomography Angiography for Aortic Pathologies in Emergency Radiology
- Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
- Large-Scale Model Validation in Healthcare
- Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT
- Predictive values of AI-based triage model in suboptimal CT pulmonary angiography
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
- Successful creation of clinical AI without data scientists or software developers: radiologist-trained AI model for identifying suboptimal chest-radiographs
2021
- A Deep Learning–based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience
- A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records
- Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study
- Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas
- Data Management in Artificial Intelligence–Assisted Radiology Reporting
- Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19
- Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports
- Federated learning for predicting clinical outcomes in patients with COVID-19
- Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues
- Lessons Learned From the Front Lines of Artificial Intelligence Implementation
- Lumbar MRI Reporting Efficiency for Trainees Over the Academic Year: An Opportunity for Improving Clinical Workflows in Academic Medical Centers
- Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
- Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study
- The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
- The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications
2020
- An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems
- CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia
- Federated Learning for Breast Density Classification: A Real-World Implementation
- Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19
- Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves
- Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels
- Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets
2019
2018
- Deep Spine: Automated Lumbar Vertebral Segmentation, Disc-level Designation and Spinal Stenosis Grading Using Deep Learning
- Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
- Machine Learning in Radiology: Applications beyond Image Interpretation
- The Need for a Machine Learning Curriculum for Radiologists
2017
2023
- Automatic segmentation and measurement of tracheal collapsibility in tracheomalacia
- Detection of idiopathic normal pressure hydrocephalus on head CT using a deep convolutional neural network
- Head CT deep learning model is highly accurate for early infarct estimation
2022
- Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model
- Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency
- Complex Relationship Between Artificial Intelligence and CT Radiation Dose
- Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas
- Correlating malignancy risk from an artificial intelligence (AI) algorithm and lung-rads-based classification from screening low-dose CT imaging
- Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging
- Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
- FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies
- Frequency of Missed Findings on Chest Radiographs (CXRs) in an International, Multicenter Study: Application of AI to Reduce Missed Findings
- Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
- Large-Scale Model Validation in Healthcare
- Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
- Performance of threshold-based stone segmentation and radiomics for determining the composition of kidney stones from single-energy CT
- Radiologist-Trained and -Tested (R2.2.4) Deep Learning Models for Identifying Anatomical Landmarks in Chest CT
- Successful creation of clinical AI without data scientists or software developers: radiologist-trained AI model for identifying suboptimal chest-radiographs
- Use of Artificial Intelligence in Clinical Neurology
- Validation pipeline for machine learning algorithm assessment for multiple vendors
2021
- A Deep Learning–based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a Multisite Experience
- A Fully Automated Deep Learning Pipeline for Multi–Vertebral Level Quantification and Characterization of Muscle and Adipose Tissue on Chest CT Scans
- A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records
- Artificial Intelligence Enabling Radiology Reporting
- Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas
- Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19
- Effects of age and sex on the distribution and symmetry of lumbar spinal and neural foraminal stenosis: a natural language processing analysis of 43,255 lumbar MRI reports
- Federated learning for predicting clinical outcomes in patients with COVID-19
- Investigating centering, scan length, and arm position impact on radiation dose across 4 countries from 4 continents during pandemic: Mitigating key radioprotection issues
- Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
- Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves
- The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
- The Trials and Tribulations of Assembling Large Medical Imaging Datasets for Machine Learning Applications
- Use of Machine Learning Algorithms to Determine Features of Systolic Blood Pressure Variability that Predict Poor Outcomes in Hypertensive Patients
2020
- Federated Learning for Breast Density Classification: A Real-World Implementation
- Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19
- Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents
- Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels
- Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets
2018
- Deep Spine: Automated Lumbar Vertebral Segmentation, Disc-level Designation and Spinal Stenosis Grading Using Deep Learning
- Machine Learning in Radiology: Applications beyond Image Interpretation
- The Need for a Machine Learning Curriculum for Radiologists
2017
2022
- An alternative to the black box: Strategy learning
- Imaging Information Overload: Quantifying the Burden of Interpretive and Non-Interpretive Tasks for Computed Tomography Angiography for Aortic Pathologies in Emergency Radiology
- Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?
2021
- Artificial Intelligence Enabling Radiology Reporting
- Data Management in Artificial Intelligence–Assisted Radiology Reporting
- Lessons Learned From the Front Lines of Artificial Intelligence Implementation
- Lumbar MRI Reporting Efficiency for Trainees Over the Academic Year: An Opportunity for Improving Clinical Workflows in Academic Medical Centers
- The Composite Severity Score for Lumbar Spine MRI: a Metric of Cumulative Degenerative Disease Predicts Time Spent on Interpretation and Reporting
2020
- An Overview and Case Study of the Clinical AI Model Development Life Cycle for Healthcare Systems
- Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents
2019
2018
- Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks
- Machine Learning in Radiology: Applications beyond Image Interpretation
- Implementing the DICOM Standard for Digital Pathology
- The Need for a Machine Learning Curriculum for Radiologists
2017
