Evaluating the impact of artificial intelligence tools on the detection of chest injuries from medical imaging: a systematic review and meta-analysis
File(s)
Author(s)
Type
Journal Article
Abstract
BACKGROUND
There has been a growing interest in the clinical application of artificial intelligence (AI) tools in medical imaging to aid diagnosis. This study conducts a systematic review of existing literature and performs a meta-analysis to compare the diagnostic performance of unassisted clinicians (CU) with clinicians assisted with AI (CA) in detecting traumatic chest injuries on diagnostic imaging.
METHODS
This systematic review was registered on the international Prospective Register of Systematic Reviews (CRD42024568478). A literature search was conducted on Ovid Medline, Ovid Embase, and the IEEE Xplore digital library, which included all studies evaluating the diagnostic performance of AI compared with a clinician for the detection of traumatic chest injuries on imaging in adults. The risk of bias was assessed using the quality assessment tool for diagnostic accuracy studies (QUADAS-2). Comparison between CA and CU groups was performed using meta-analysis for the primary outcome of diagnostic sensitivity and diagnostic time (DT) as a secondary outcome, with mean difference used as the effect measure.
RESULTS
The search strategy identified 6,013 records. Following a full-text review, 20 studies were included, with 12 suitable for meta-analysis for rib fracture detection. The use of AI was associated with an improvement in sensitivity (CA, 0.88; CU, 0.76; mean difference, 0.12) and a reduction in DT (DT CA, 115 seconds; DT CU, 214 seconds; mean difference, −99 seconds).
CONCLUSION
Artificial intelligence assistance can improve the diagnostic performance of clinicians. Clinicians assisted with AI were associated with an increase in the diagnostic sensitivity with a reduction in the DT to detect rib fractures on clinical imaging compared with CU. However, the overall quality of the evidence is poor, and further research into clinically useful models is required.
There has been a growing interest in the clinical application of artificial intelligence (AI) tools in medical imaging to aid diagnosis. This study conducts a systematic review of existing literature and performs a meta-analysis to compare the diagnostic performance of unassisted clinicians (CU) with clinicians assisted with AI (CA) in detecting traumatic chest injuries on diagnostic imaging.
METHODS
This systematic review was registered on the international Prospective Register of Systematic Reviews (CRD42024568478). A literature search was conducted on Ovid Medline, Ovid Embase, and the IEEE Xplore digital library, which included all studies evaluating the diagnostic performance of AI compared with a clinician for the detection of traumatic chest injuries on imaging in adults. The risk of bias was assessed using the quality assessment tool for diagnostic accuracy studies (QUADAS-2). Comparison between CA and CU groups was performed using meta-analysis for the primary outcome of diagnostic sensitivity and diagnostic time (DT) as a secondary outcome, with mean difference used as the effect measure.
RESULTS
The search strategy identified 6,013 records. Following a full-text review, 20 studies were included, with 12 suitable for meta-analysis for rib fracture detection. The use of AI was associated with an improvement in sensitivity (CA, 0.88; CU, 0.76; mean difference, 0.12) and a reduction in DT (DT CA, 115 seconds; DT CU, 214 seconds; mean difference, −99 seconds).
CONCLUSION
Artificial intelligence assistance can improve the diagnostic performance of clinicians. Clinicians assisted with AI were associated with an increase in the diagnostic sensitivity with a reduction in the DT to detect rib fractures on clinical imaging compared with CU. However, the overall quality of the evidence is poor, and further research into clinically useful models is required.
Date Issued
2026-04-01
Date Acceptance
2025-11-23
Citation
Journal of Trauma and Acute Care Surgery, 2026, 100 (4), pp.660-670
ISSN
2163-0755
Publisher
Lippincott, Williams & Wilkins
Start Page
660
End Page
670
Journal / Book Title
Journal of Trauma and Acute Care Surgery
Volume
100
Issue
4
Copyright Statement
© 2026 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Surgery of Trauma. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1097/TA.0000000000004891
Subjects
Artificial intelligence
chest computed tomography
diagnostic imaging
thoracic trauma
Publication Status
Published
Date Publish Online
2026-02-12