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Articles Published Processes
4/10/2026 7:08:44 AM | Browse: 41 | Download: 110
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Received |
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2025-09-01 05:27 |
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Peer-Review Started |
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2025-09-01 05:27 |
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First Decision by Editorial Office Director |
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2025-09-17 08:30 |
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Return for Revision |
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2025-09-17 08:30 |
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Revised |
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2025-10-02 00:09 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2026-01-14 02:35 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-01-14 09:59 |
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Articles in Press |
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2026-01-14 09:59 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-03-30 09:40 |
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Publish the Manuscript Online |
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2026-04-10 07:08 |
| ISSN |
2218-5836 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Orthopedics |
| Manuscript Type |
Retrospective Cohort Study |
| Article Title |
From subtle breaks to missed diagnoses: Real-world evaluation of an artificial intelligence fracture detection tool
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Sari Wathiq Al Hajaj, Khaled Soliman, Mahira Zafar, Callum Garnham, Dawod Al Hajaj, Omar Elshafie, Ahmad Alsswah and Mohammed H Elwan |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Sari Wathiq Al Hajaj, MD, Department of Trauma and Orthopaedics, Kettering General Hospital, NHS Foundation Trust, Kettering NN16 8UZ, Northamptonshire, United Kingdom. sarialhajaj95@gmail.com |
| Key Words |
Artificial intelligence; Fractures; Bone/diagnostic imaging; Radiography; Emergency service; Hospital; Diagnostic errors; Sensitivity and specificity; Retrospective studies; Humans |
| Core Tip |
Artificial intelligence (AI) is increasingly utilised in musculoskeletal imaging; however, its clinical utility in fracture detection remains a subject of debate. In this retrospective study aimed at assessing diagnostic accuracy, we evaluated an AI-based fracture detection system in comparison with radiologist reports across over 2000 limb radiographs. The AI demonstrated high accuracy but failed to identify approximately 10% of fractures, primarily subtle or minimally displaced injuries, and frequently overdiagnosed old or degenerative changes. False negatives posed a greater clinical risk than false positives, underscoring the importance of implementing AI as a triage and decision-support tool rather than a substitute for radiologists. |
| Publish Date |
2026-04-10 07:08 |
| Citation |
Al Hajaj SW, Soliman K, Zafar M, Garnham C, Al Hajaj D, Elshafie O, Alsswah A, Elwan MH. From subtle breaks to missed diagnoses: Real-world evaluation of an artificial intelligence fracture detection tool. World J Orthop 2026; 17(4): 113710 |
| URL |
https://www.wjgnet.com/2218-5836/full/v17/i4/113710.htm |
| DOI |
https://dx.doi.org/10.5312/wjo.v17.i4.113710 |
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