Evidence

Driving adoption with strong scientific evidence

BoneView
  • 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
  • Regnard et. al,
  • RSNA

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

BoneView
  • 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
  • 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

Interested in Research collaboration

Tell us about your project