Evidence
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BoneView
Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception. A Prospective Pre- and Post-Analysis
Hoppe, Boj Friedrich MD; Rueckel, Johannes MD; Dikhtyar, Yevgeniy MD; Heimer, Maurice MD; Fink, Nicola MD; Sabel, Bastian Oliver MD; Ricke, Jens MD; Rudolph, Jan MD; Cyran, Clemens C. MD -
ChestView
Learning from the machine: AI assistance is not an effective learning tool for resident education in chest x-ray interpretation
Chassagnon G, Billet N, Rutten C, Toussaint T, Cassius de Linval Q, Collin M et al. -
BoneView
Artificial intelligence and pelvic fracture diagnosis on X-rays: a preliminary study on performance, workflow integration and radiologists’ feedback assessment in a spoke emergency hospital
Rosa F, Buccicardi D, Romano A, Borda F, D’Auria MC, Gastaldo -
BoneAge
High performances in bone age estimation using an artificial intelligence solution
Nguyen T, Hermann AL, Ventre J, Ducarouge A, Pourchot A, Marty V et al. -
BoneView
A Prospective Approach to Integration of AI Fracture Detection Software in Radiographs into Clinical Workflow
Oppenheimer J, Lüken S, Hamm B, Niehues SM. A -
BoneView
Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs
Cohen M, Puntonet J, Sanchez J, Kierszbaum E, Crema M, Soyer P et al. -
BoneView
Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists
Nguyen T, Maarek R, Hermann AL, Kammoun A, Marchi A, Khelifi-Touhami MR et al. -
BoneView
Assessment of performances of a deep learning algorithm for the detection of limbs and pelvic fractures, dislocations, focal bone lesions, and elbow effusions on trauma X-rays
Regnard NE, Lanseur B, Ventre J, Ducarouge A, Clovis L, Lassalle L et al.