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A Prospective Approach to Integration of AI Fracture Detection Software in Radiographs into Clinical Workflow

Gleamer BoneView© is a commercially available AI algorithm for fracture detection in radiographs. We aim to test if the algorithm can assist in better sensitivity and specificity for fracture detection by residents with prospective integration into clinical workflow. Radiographs with inquiry for fracture initially reviewed by two residents were randomly assigned and included. A preliminary diagnosis of a possible fracture was made. Thereafter, the AI decision on presence and location of possible fractures was shown and changes to diagnosis could be made. Final diagnosis of fracture was made by a board-certified radiologist with over eight years of experience, or if available, cross-sectional imaging. Sensitivity and specificity of the human report, AI diagnosis, and assisted report were calculated in comparison to the final expert diagnosis. 1163 exams in 735 patients were included, with a total of 367 fractures (31.56%). Pure human sensitivity was 84.74%, and AI sensitivity was 86.92%. Thirty-five changes were made after showing AI results, 33 of which resulted in the correct diagnosis, resulting in 25 additionally found fractures. This resulted in a sensitivity of 91.28% for the assisted report. Specificity was 97.11, 84.67, and 97.36%, respectively. AI assistance showed an increase in sensitivity for both residents, without a loss of specificity.


BoneView, our first clinical AI application, has become a global bone trauma X-ray interpretation standard, recognized for its scientific excellence. It pinpoints fractures, effusions, dislocations, and bone lesions efficiently. Recognized for its scientific rigor with publications in top-tier peer-reviewed journals, its clinical study won the prestigious 2022 Alexander Margulis Award for scientific excellence.

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Boneview V2