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The British Journal of Radiology
|
June 18, 2021
Improving reference standards for validation of AI-based radiography
Gavin E Duggan, Joshua J Reicher, Yun Liu, et al.
Nature
|
October 15, 2020
Reply to: Transparency and reproducibility in artificial intelligence
Scott Mayer McKinney, Alan Karthikesalingam, Daniel Tse, et al.
JAMA Network Open
|
January 4, 2023
Development of a Machine Learning Model for Sonographic Assessment of Gestational Age
Chace Lee, Angelica Willis, Christina Chen, et al.
Nature Medicine
|
May 22, 2019
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, et al.
Nature Medicine
|
June 30, 2019
Author Correction: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, et al.
Radiology
|
December 4, 2019
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation
Anna Majkowska, Sid Mittal, David F Steiner, et al.
Radiology
|
July 19, 2022
Simplified Transfer Learning for Chest Radiography Models Using Less Data
Andrew B Sellergren, Christina Chen, Zaid Nabulsi, et al.
NEJM AI
|
January 17, 2025
Prospective Multi-Site Validation of AI to Detect Tuberculosis and Chest X-Ray Abnormalities
Sahar Kazemzadeh, Atilla P Kiraly, Zaid Nabulsi, et al.
Scientific Reports
|
September 2, 2021
Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19
Zaid Nabulsi, Andrew Sellergren, Shahar Jamshy, et al.
Radiology
|
September 6, 2022
Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists
Sahar Kazemzadeh, Jin Yu, Shahar Jamshy, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 16) with videos related to
Sort By:
Page
of 2
The British Journal of Radiology
|
June 18, 2021
Improving reference standards for validation of AI-based radiography
Gavin E Duggan, Joshua J Reicher, Yun Liu, et al.
Nature
|
October 15, 2020
Reply to: Transparency and reproducibility in artificial intelligence
Scott Mayer McKinney, Alan Karthikesalingam, Daniel Tse, et al.
JAMA Network Open
|
January 4, 2023
Development of a Machine Learning Model for Sonographic Assessment of Gestational Age
Chace Lee, Angelica Willis, Christina Chen, et al.
Nature Medicine
|
May 22, 2019
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, et al.
Nature Medicine
|
June 30, 2019
Author Correction: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
Diego Ardila, Atilla P Kiraly, Sujeeth Bharadwaj, et al.
Radiology
|
December 4, 2019
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation
Anna Majkowska, Sid Mittal, David F Steiner, et al.
Radiology
|
July 19, 2022
Simplified Transfer Learning for Chest Radiography Models Using Less Data
Andrew B Sellergren, Christina Chen, Zaid Nabulsi, et al.
NEJM AI
|
January 17, 2025
Prospective Multi-Site Validation of AI to Detect Tuberculosis and Chest X-Ray Abnormalities
Sahar Kazemzadeh, Atilla P Kiraly, Zaid Nabulsi, et al.
Scientific Reports
|
September 2, 2021
Deep learning for distinguishing normal versus abnormal chest radiographs and generalization to two unseen diseases tuberculosis and COVID-19
Zaid Nabulsi, Andrew Sellergren, Shahar Jamshy, et al.
Radiology
|
September 6, 2022
Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists
Sahar Kazemzadeh, Jin Yu, Shahar Jamshy, et al.
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of 2