JORR
Journal of Orthopedics Research and Rehabilitation

The Journal of Orthopedics Research and Rehabilitation welcomes scholarly papers in orthopaedic surgery, physical therapy and rehabilitation, physiotherapy, neurology and clinic anesthesiology and reanimation.

eISSN: 2980-0560
Creative Commons CC BY-NC-ND 4.0 International License.
EndNote Style

Online Manuscript System

Index
Letter to the Editor
Artificial Intelligence in fracture detection: is it time to redefine the role of the orthopedic surgeon?
Abstract
Artificial Intelligence (AI) applications in radiology have demonstrated diagnostic accuracy comparable to human experts in fracture detection. Recent studies have shown that AI-assisted systems not only match but sometimes exceed the performance of junior clinicians and non-specialist readers, particularly in identifying subtle or occult fractures. However, evidence suggests that AI performs best when used alongside human oversight rather than as a standalone diagnostic tool. The role of the orthopedic surgeon-encompassing clinical judgment, physical examination, and surgical decision-making-remains irreplaceable. Rather than redefining this role, AI should be strategically integrated to support decision-making and improve diagnostic efficiency. This letter highlights the evolving landscape of fracture diagnostics and emphasizes the need for collaborative synergy between human expertise and machine intelligence.

Keywords : Artificial Intelligence, fracture detection, orthopedic surgery

Download Article


1. Kuo RYL, Harrison C, Curran TA, et al. Artificial Intelligence in fracture detection: a systematic review and meta-analysis. Radiology. 2022;304(1):50-62. doi:10.1148/radiol.211785
2. Husarek J, Hess S, Razaeian S, et al. Artificial Intelligence in commercial fracture detection products: a systematic review and meta-analysis of diagnostic test accuracy. Sci Rep. 2024;14(1):23053. doi:10.1038/s41598-024-73058-8
3. Liu Y, Liu W, Chen H, et al. Artificial Intelligence versus radiologist in the accuracy of fracture detection based on computed tomography images: a multi-dimensional, multi-region analysis. Quant Imaging Med Surg. 2023;13(10):6424-6433. doi:10.21037/qims-23-428
4. Meetschen M, Salhöfer L, Beck N, et al. AI-assisted X-Ray fracture detection in residency training: evaluation in pediatric and adult trauma patients. Diagnostics (Basel). 2024;14(6):596. doi:10.3390/diagnostics14060596
5. Bachmann R, Gunes G, Hangaard S, et al. Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study. BJR Open. 2024;6(1):tzae011. doi:10.1093/bjro/tzae011
Article available in :
Volume 3, Issue 4, 2025
Page : 103-104
https://doi.org/10.51271/JORR-0061
Article Information
Received : 02 Haz 2025
Accepted : 11 Eki 2025
Published : 22 Eki 2025
Corresponding Author :

Humam Baki: Department of Orthopedics and Traumatology, Private Gaziosmanpaşa Hospital, İstanbul Yeni Yüzyıl University, İstanbul, Turkiye

[email protected]
Citation : Baki H. Letter to the editor: comment on “Artificial Intelligence in fracture detection: is it time to redefine the role of the orthopedic surgeon?” J Orthop Res Rehabil. 2025;3(4):103-104.

Read: 232
Download : 411
Cited: 0
_Footer