Artificial intelligence in virtual fracture clinics: a systematic review of imaging and clinical-text tools
Author(s)
Sukhbaatar, Tenghis
Davies, Andrew
Koye, Aran
Hashem, Mohamed
Sivaloganathan, Sivan
Type
Journal Article
Abstract
Background
Virtual fracture clinics (VFCs) are a well-established component of acute orthopedic management pathways.
Artificial intelligence (AI) healthcare tools are increasingly sophisticated and have the potential to disrupt current practices. The aim of this review was to determine the opportunities, performance and readiness of AI systems that integrate clinical-text and imaging data for the triage or management of patients in VFCs.
Methods
A search of MEDLINE and Embase was performed between January 2010 and July 2025. The review included primary research studies investigating AI for fracture detection via X-rays and natural language processing (NLP) models for clinical documentation. A random-effects meta-analysis was conducted to calculate pooled sensitivity and specificity, stratified by anatomical region and developer type (commercial vs. researcher-developed).
Results
We included 54 studies: 52 imaging/X-ray studies and 2 NLP/clinical-text studies. Among the imaging studies,
13 evaluated commercial tools, and 39 evaluated researcher-developed models. There were 2 NLP models, both of which interpreted radiology reports rather than text summaries of clinical assessments. No studies that included the use of NLP models in acute orthopedic care could be found. A meta-analysis of commercial tools (n=11) demonstrated a pooled sensitivity across both multiregional "Limb" tools of 92.58% (95% CI: 90.61–94.17%) and anatomy-specific "Wrist" tools of 89.95% (95% CI: 72.18–96.86%). Wrist-specific commercial tools demonstrated higher specificity (96.08%; 95% CI: 90.12–99.01%) compared to general limb tools (89.69%; 95% CI: 84.02–93.51%), suggesting that anatomical targeting may reduce the number of false positives. Researcher-developed models (n=32) often reported superior metrics for sensitivity (e.g., Limbs 95.11%; 95% CI: 91.83–
97.11%) compared to commercial tools sensitivity.
Conclusions
VFCs require the integration of information from imaging and patient records. Multiple image interpretation
tools are available with high performance in fracture identification. The development and integration of NLP
tools to interpret clinical documentation from emergency departments and urgent care centers are necessary
for AI-VFC.
Virtual fracture clinics (VFCs) are a well-established component of acute orthopedic management pathways.
Artificial intelligence (AI) healthcare tools are increasingly sophisticated and have the potential to disrupt current practices. The aim of this review was to determine the opportunities, performance and readiness of AI systems that integrate clinical-text and imaging data for the triage or management of patients in VFCs.
Methods
A search of MEDLINE and Embase was performed between January 2010 and July 2025. The review included primary research studies investigating AI for fracture detection via X-rays and natural language processing (NLP) models for clinical documentation. A random-effects meta-analysis was conducted to calculate pooled sensitivity and specificity, stratified by anatomical region and developer type (commercial vs. researcher-developed).
Results
We included 54 studies: 52 imaging/X-ray studies and 2 NLP/clinical-text studies. Among the imaging studies,
13 evaluated commercial tools, and 39 evaluated researcher-developed models. There were 2 NLP models, both of which interpreted radiology reports rather than text summaries of clinical assessments. No studies that included the use of NLP models in acute orthopedic care could be found. A meta-analysis of commercial tools (n=11) demonstrated a pooled sensitivity across both multiregional "Limb" tools of 92.58% (95% CI: 90.61–94.17%) and anatomy-specific "Wrist" tools of 89.95% (95% CI: 72.18–96.86%). Wrist-specific commercial tools demonstrated higher specificity (96.08%; 95% CI: 90.12–99.01%) compared to general limb tools (89.69%; 95% CI: 84.02–93.51%), suggesting that anatomical targeting may reduce the number of false positives. Researcher-developed models (n=32) often reported superior metrics for sensitivity (e.g., Limbs 95.11%; 95% CI: 91.83–
97.11%) compared to commercial tools sensitivity.
Conclusions
VFCs require the integration of information from imaging and patient records. Multiple image interpretation
tools are available with high performance in fracture identification. The development and integration of NLP
tools to interpret clinical documentation from emergency departments and urgent care centers are necessary
for AI-VFC.
Date Acceptance
2026-12-30
Citation
Journal of Orthopaedic Surgery and Research
ISSN
1749-799X
Publisher
BMC
Journal / Book Title
Journal of Orthopaedic Surgery and Research
Copyright Statement
Copyright This paper is embargoed until publication. Once published the Version of Record (VoR) will be available on immediate open access.
License URL
Publication Status
Accepted