Search research articles
Contact Us
Filters
Showing results (1-10 of 13) with videos related to
Page
of 2
Sort By:
BMJ Health & Care Informatics
|
April 27, 2025
Potential for near-term AI risks to evolve into existential threats in healthcare
Vallijah Subasri, Negin Baghbanzadeh, Leo Anthony Celi, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|
March 10, 2021
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, et al.
Trends in Cancer
|
December 3, 2025
Rethinking fairness in AI to improve current practice in oncology
Salamata Konate, Jack Gallifant, Charles Senteio, et al.
Nature Medicine
|
December 11, 2021
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Laleh Seyyed-Kalantari, Haoran Zhang, Matthew B A McDermott, et al.
Radiology. Artificial Intelligence
|
October 5, 2023
The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups
Monish Ahluwalia, Mohamed Abdalla, James Sanayei, et al.
Current Problems in Diagnostic Radiology
|
February 1, 2024
Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective
Vineela Nalla, Seyedamin Pouriyeh, Reza M Parizi, et al.
Journal of the American College of Radiology : JACR
|
July 28, 2023
"Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation
Imon Banerjee, Kamanasish Bhattacharjee, John L Burns, et al.
Computational and Structural Biotechnology Journal
|
March 3, 2025
Interpretability of AI race detection model in medical imaging with saliency methods
Salamata Konate, Léo Lebrat, Rodrigo Santa Cruz, et al.
PLOS Digital Health
|
April 8, 2025
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography
MinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, et al.
Frontiers in Radiology
|
August 17, 2023
Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography
InChan Hwang, Hari Trivedi, Beatrice Brown-Mulry, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 13) with videos related to
Sort By:
Page
of 2
BMJ Health & Care Informatics
|
April 27, 2025
Potential for near-term AI risks to evolve into existential threats in healthcare
Vallijah Subasri, Negin Baghbanzadeh, Leo Anthony Celi, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|
March 10, 2021
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, et al.
Trends in Cancer
|
December 3, 2025
Rethinking fairness in AI to improve current practice in oncology
Salamata Konate, Jack Gallifant, Charles Senteio, et al.
Nature Medicine
|
December 11, 2021
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Laleh Seyyed-Kalantari, Haoran Zhang, Matthew B A McDermott, et al.
Radiology. Artificial Intelligence
|
October 5, 2023
The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology Subgroups
Monish Ahluwalia, Mohamed Abdalla, James Sanayei, et al.
Current Problems in Diagnostic Radiology
|
February 1, 2024
Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective
Vineela Nalla, Seyedamin Pouriyeh, Reza M Parizi, et al.
Journal of the American College of Radiology : JACR
|
July 28, 2023
"Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation
Imon Banerjee, Kamanasish Bhattacharjee, John L Burns, et al.
Computational and Structural Biotechnology Journal
|
March 3, 2025
Interpretability of AI race detection model in medical imaging with saliency methods
Salamata Konate, Léo Lebrat, Rodrigo Santa Cruz, et al.
PLOS Digital Health
|
April 8, 2025
Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography
MinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, et al.
Frontiers in Radiology
|
August 17, 2023
Impact of multi-source data augmentation on performance of convolutional neural networks for abnormality classification in mammography
InChan Hwang, Hari Trivedi, Beatrice Brown-Mulry, et al.
Page
of 2