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European Journal of Radiology|March 8, 2022
Artificial Intelligence based detection of pneumoperitoneum on CT scans in patients presenting with acute abdominal pain: A clinical diagnostic test accuracy studyMathias W Brejnebøl, Yousef W Nielsen, Oliver Taubmann, et al.
Scientific Reports|November 5, 2024
Artificial intelligence tools trained on human-labeled data reflect human biases: a case study in a large clinical consecutive knee osteoarthritis cohortAnders Lenskjold, Mathias W Brejnebøl, Martin H Rose, et al.
Osteoarthritis and Cartilage|December 3, 2023
Constructing a clinical radiographic knee osteoarthritis database using artificial intelligence tools with limited human labor: A proof of principleAnders Lenskjold, Mathias W Brejnebøl, Janus U Nybing, et al.
Radiology|September 26, 2023
Commercially Available Chest Radiograph AI Tools for Detecting Airspace Disease, Pneumothorax, and Pleural EffusionLouis Lind Plesner, Felix C Müller, Mathias W Brejnebøl, et al.
Radiology|August 20, 2024
Using AI to Identify Unremarkable Chest Radiographs for Automatic ReportingLouis Lind Plesner, Felix C Müller, Mathias W Brejnebøl, et al.
European Journal of Radiology|October 7, 2023
Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non-comprehensive stroke centerChristian H Krag, Felix C Müller, Karen L Gandrup, et al.
Radiology|July 9, 2024
Interobserver Agreement and Performance of Concurrent AI Assistance for Radiographic Evaluation of Knee OsteoarthritisMathias W Brejnebøl, Anders Lenskjold, Katharina Ziegeler, et al.
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