Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence

Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence

Background: Missed fractures are a common cause of diagnostic discrepancy between initial radiographic interpretation and the final read by board-certified radiologists. Purpose: To assess the effect of assistance by artificial intelligence (AI) on diagnostic performances of physicians for fractures...

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Bibliographic Details
Main Authors: Comeau, D. (Author), Ducarouge, A. (Author), Gillibert, A. (Author), Guermazi, A. (Author), Hayashi, D. (Author), Jarraya, M. (Author), Kompel, A.J (Author), Lacave, E. (Author), Lahoud, Y. (Author), Li, X. (Author), Merritt, A.C (Author), Murakami, A.M (Author), Parisien, R.L (Author), Pourchot, A. (Author), Rahimi, H. (Author), Regnard, N.-E (Author), Tannoury, C. (Author), Tournier, A. (Author)
Format: Article
Language:English
Published: Radiological Society of North America Inc. 2022
Subjects:
adult
ankle radiography
Article
artificial intelligence
Artificial Intelligence
bone radiography
clinical effectiveness
computer assisted diagnosis
Datasets as Topic
diagnostic error
Diagnostic Errors
diagnostic imaging
diagnostic test accuracy study
diagnostic value
elbow radiography
female
Female
foot radiography
fracture
Fractures, Bone
hand radiography
hip radiography
human
Humans
information processing
knee radiography
major clinical study
male
Male
middle aged
Middle Aged
musculoskeletal radiologist
pelvis radiography
prevalence
prevention and control
procedures
Quality Improvement
Radiographic Image Interpretation, Computer-Assisted
receiver operating characteristic
Retrospective Studies
retrospective study
sensitivity and specificity
Sensitivity and Specificity
shoulder radiography
spine radiography
total quality management
wrist radiography
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