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An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload
Andreas D Lauritzen
, Alejandro Rodríguez-Ruiz
, My Catarina von Euler-Chelpin
, Elsebeth Lynge
, Ilse Vejborg
, Mads Nielsen
, Nico Karssemeijer
, Martin Lillholm
*
*
Corresponding author for this work
Centre for Health Research
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Explore the research areas of 'An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload'.
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Keyphrases
Radiologists
100%
Mammography Screening
100%
Artificial Intelligence
100%
Screening Protocol
100%
Breast Cancer Outcomes
100%
Mammogram
50%
Data Systems
30%
Breast Imaging
30%
Reporting System
30%
Moderate Risk
20%
Screening Sensitivity
20%
Positive Screening
20%
Artificial Intelligence Systems
20%
Breast Cancer Screening Programme
20%
Capital Region
10%
Sensitivity Specificity
10%
Non-inferiority
10%
Breast Density
10%
Breast Cancer Screening
10%
Simulation Study
10%
False Positive Rate
10%
Screen-detected
10%
Mammogram Images
10%
McNemar Test
10%
Screening Outcomes
10%
Screening for Breast Cancer
10%
Risk of Malignancy
10%
Normal Mammograms
10%
Screening Efficiency
10%
Retrospective Simulation
10%
Supplemental Materials
10%
Artificial Intelligence Tools
10%
Medicine and Dentistry
Breast Cancer
100%
False Positive Result
100%
Mammography
100%
Breast Imaging - Reporting And Data System
100%
Breast Cancer Screening
100%
Cancer
66%
Breast Density
33%
McNemar Test
33%