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Scott Mayer McKinney

Showing results (1-10 of 9) with videos related to

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Nature|October 15, 2020
Reply to: Transparency and reproducibility in artificial intelligenceScott Mayer McKinney, Alan Karthikesalingam, Daniel Tse, et al.
Radiology|December 4, 2019
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted EvaluationAnna Majkowska, Sid Mittal, David F Steiner, et al.
Ophthalmology. Retina|January 9, 2022
Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation StudyXinle Liu, Tayyeba K Ali, Preeti Singh, et al.
Radiology|September 6, 2022
Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of RadiologistsSahar Kazemzadeh, Jin Yu, Shahar Jamshy, et al.
Breast Cancer Research and Treatment|January 29, 2025
Triaging mammography with artificial intelligence: an implementation studySarah M Friedewald, Marcin Sieniek, Sunny Jansen, et al.
Communications Medicine|October 17, 2022
A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessmentRyan G Gomes, Bellington Vwalika, Chace Lee, et al.
Nature|October 15, 2020
Addendum: International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature|January 3, 2020
International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature Biomedical Engineering|June 8, 2023
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imagingShekoofeh Azizi, Laura Culp, Jan Freyberg, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Nature|October 15, 2020
Reply to: Transparency and reproducibility in artificial intelligenceScott Mayer McKinney, Alan Karthikesalingam, Daniel Tse, et al.
Radiology|December 4, 2019
Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted EvaluationAnna Majkowska, Sid Mittal, David F Steiner, et al.
Ophthalmology. Retina|January 9, 2022
Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation StudyXinle Liu, Tayyeba K Ali, Preeti Singh, et al.
Radiology|September 6, 2022
Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of RadiologistsSahar Kazemzadeh, Jin Yu, Shahar Jamshy, et al.
Breast Cancer Research and Treatment|January 29, 2025
Triaging mammography with artificial intelligence: an implementation studySarah M Friedewald, Marcin Sieniek, Sunny Jansen, et al.
Communications Medicine|October 17, 2022
A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessmentRyan G Gomes, Bellington Vwalika, Chace Lee, et al.
Nature|October 15, 2020
Addendum: International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature|January 3, 2020
International evaluation of an AI system for breast cancer screeningScott Mayer McKinney, Marcin Sieniek, Varun Godbole, et al.
Nature Biomedical Engineering|June 8, 2023
Robust and data-efficient generalization of self-supervised machine learning for diagnostic imagingShekoofeh Azizi, Laura Culp, Jan Freyberg, et al.
Pageof 1