Evidence

Driving adoption with strong scientific evidence

ChestView
  • Bennani et. al,
  • ESTI 2022

Evaluation of radiologists’ performance compared to a deep learning algorithm for the detection of thoracic abnormalities on chest X-ray

ChestView
  • Bennani et. al,
  • ECR 2023

Evaluation of radiologists’ performance compared to a deep learning algorithm for the detection of thoracic abnormalities on chest X-ray

BoneView Trauma
  • Ali Guermazi ,
  • Radiology

Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence

BoneView Trauma
  • ECR 2022

Artificial Intelligence (AI) support for pelvic fracture detection on plain radiographs: a preliminary study of AI integration in the clinical workflow

BoneView Trauma
  • Maarek et. al,
  • ECR 2022

Assessment of an AI aid in detection of pediatric appendicular skeletal fractures by senior and junior radiologists.

BoneView Trauma
  • Hayashi et. al,
  • RSNA

Improving Radiographic Fracture Detection and Reducing Reading Time Using Artificial Intelligence: A Multi-Center Study with Radiologists and Non-Radiologists in The United States

BoneView Trauma
  • Regnard et. al,
  • RSNA

Evaluation of the medical impact of artificial intelligence for limb and pelvic bone fracture detection

BoneView Trauma
  • Regnard et. al,
  • EuSoMII 2021

Performances of a deep learning algorithm for the detection of fracture, dislocation, elbow joint effusion, focal bone lesions on trauma X-rays

  • Omoumi et. al,
  • European Radiology

To buy or not to buy—evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

BoneView Trauma
  • Duron et al.,
  • Radiology

Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study

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