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Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

PLoS medicine, 2018-11, Vol.15 (11), p.e1002686-e1002686

2018

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The Future of AI and Informatics in Radiology: 10 Predictions

Radiology, 2023-10, Vol.309 (1), p.e231114

Langlotz, Curtis P

2023

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Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

PLoS medicine, 2018-11, Vol.15 (11), p.e1002699-e1002699

2018

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Will Artificial Intelligence Replace Radiologists?

Radiology. Artificial intelligence, 2019-05, Vol.1 (3), p.e190058-e190058

Langlotz, Curtis P

2019

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Information extraction from multi-institutional radiology reports

Artificial intelligence in medicine, 2016-01, Vol.66, p.29-39

2016

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Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs

Radiology, 2018-04, Vol.287 (1), p.313-322

2018

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Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

Nature biomedical engineering, 2022-12, Vol.6 (12), p.1399-1406

2022

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RadLex: a new method for indexing online educational materials

Radiographics, 2006-11, Vol.26 (6), p.1595-1597

Langlotz, Curtis P

2006

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Video-based AI for beat-to-beat assessment of cardiac function

Nature (London), 2020-04, Vol.580 (7802), p.252-256

2020

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Biomedical and clinical English model packages for the Stanza Python NLP library

Journal of the American Medical Informatics Association : JAMIA, 2021-09, Vol.28 (9), p.1892-1899

2021

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Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs

Radiology, 2019-02, Vol.290 (2), p.537-544

2019

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A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

Radiology, 2019-06, Vol.291 (3), p.781-791

2019

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Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations

European radiology, 2020-06, Vol.30 (6), p.3576-3584

2020

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Deep Learning to Classify Radiology Free-Text Reports

Radiology, 2018-03, Vol.286 (3), p.845-852

2018

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AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Scientific reports, 2020-03, Vol.10 (1), p.3958-3958, Article 3958

2020

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Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Radiology, 2022-12, Vol.305 (3), p.555-563

2022

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Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework

Radiology, 2020-06, Vol.295 (3), p.675-682

2020

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Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations

Journal of the American College of Radiology, 2021-03, Vol.18 (3), p.413-424

2021

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External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

European radiology, 2024-04, Vol.34 (4), p.2727-2737

2024

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Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

Artificial intelligence in medicine, 2019-06, Vol.97, p.79-88

2019

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"Chasing a Ghost": Factors that Influence Primary Care Physicians to Follow Up on Incidental Imaging Findings

Radiology, 2016-11, Vol.281 (2), p.567-573

2016

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Beyond the AJR : “Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model”

American journal of roentgenology (1976), 2021-08, Vol.217 (2), p.521-521

2021

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Predicting post-operative right ventricular failure using video-based deep learning

Nature communications, 2021-08, Vol.12 (1), p.5192-5192, Article 5192

2021

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Adapted large language models can outperform medical experts in clinical text summarization

Nature medicine, 2024-04, Vol.30 (4), p.1134-1142

2024

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Structured reporting: patient care enhancement or productivity nightmare?

Radiology, 2008-12, Vol.249 (3), p.739-747

2008

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Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

The Future of AI and Informatics in Radiology: 10 Predictions

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

Will Artificial Intelligence Replace Radiologists?

Information extraction from multi-institutional radiology reports

Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs

Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

RadLex: a new method for indexing online educational materials

Video-based AI for beat-to-beat assessment of cardiac function

Biomedical and clinical English model packages for the Stanza Python NLP library

Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs

A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations

Deep Learning to Classify Radiology Free-Text Reports

AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework

Regulatory Frameworks for Development and Evaluation of Artificial Intelligence–Based Diagnostic Imaging Algorithms: Summary and Recommendations

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification

"Chasing a Ghost": Factors that Influence Primary Care Physicians to Follow Up on Incidental Imaging Findings

Beyond the AJR : “Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model”

Predicting post-operative right ventricular failure using video-based deep learning

Adapted large language models can outperform medical experts in clinical text summarization

Structured reporting: patient care enhancement or productivity nightmare?

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