AI decision-support in the initial management of orthopedic trauma: a systematic review
- 1Herbert Wertheim College of Medicine, Florida International University, 11200 SW 8th Street, Miami, FL 33199 USA
- 2Department of Orthopedics Emeritus, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
Abstract
Background: Early orthopedic trauma care requires rapid, high-stakes decision-making under conditions of physiologic instability and diagnostic uncertainty. Conventional clinical scores and guideline-based tools aim to standardize early management but often demonstrate limited patient-level accuracy and variable performance across care settings. Artificial intelligence (AI) and machine learning (ML) based decision-support tools have been proposed as adjuncts to support early trauma decisions, yet their scope and performance in orthopedic trauma remain uncharacterized.
Methods: A PRISMA-guided systematic review was conducted of prediction-model and implementation studies published between January 2010 and October 2025 using MEDLINE (PubMed), Embase, and Cochrane CENTRAL. Eligible studies evaluated AI/ML-based tools designed to predict or support early orthopedic trauma decisions in the emergency department, trauma bay, or prehospital setting. Target decision domains included hemorrhage control, operative versus nonoperative management, limb salvage versus amputation, and level-of-care or interfacility triage. Data extraction focused on study design, clinical decision targets, model inputs, validation strategy and reported performance metrics.
Results: From 361 identified records, 9 studies met inclusion criteria. Study populations, modeling approaches, and clinical targets were heterogeneous. Reported discriminatory performance was generally moderate to favorable, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.67 to 0.97 across decision domains. External validation was limited, and reporting of calibration and decision-analytic metrics was inconsistent. Imaging-adjacent AI tools enabled automated computed tomography (CT) based hematoma quantification for pelvic trauma and real-time CT auto-segmentation workflows for maxillofacial trauma (Dice ≈0.92–0.93) with substantial time reduction. However, the included studies did not directly evaluate downstream effects on clinician decisions or workflow throughput beyond segmentation tasks, or patient-centered outcomes.
Conclusions: AI/ML-based decision-support tools for early orthopedic trauma management show strong technical feasibility and encouraging performance in existing validation studies, supporting their potential role as adjuncts to clinician judgment in early, high-stakes decisions. The current literature is dominated by retrospective evaluation and offers limited evidence on real-world impact. Next steps should prioritize prospective, workflow-integrated studies with standardized reporting of calibration and decision utility to determine how these tools can most effectively improve care delivery and patient outcomes in early orthopedic trauma.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13018-026-07107-5.
Related
- Large Language Models as Decision Support in Breast Cancer Management: A Comparison with Multidisciplinary Team Decision-Making
- Evaluating a Clinical Decision Support System for Chronic Pain Management: A Per-Protocol Analysis
- Shared Decision-Making and Decision Support in Advanced Chronic Kidney Disease: A Systematic Review of Models, Treatment Pathways, and Patient-Centered Outcomes
- Comparative effectiveness of exercise and self-management interventions delivered through different modes of digital support on pain and patient-reported physical function in people with knee osteoarthritis: a systematic review and network meta-analysis of randomised controlled trials
- Fairness in multimodal machine learning applications in clinical decision support: a systematic review
BibTeX
@article{jasti2026decisionsupport,
title = {{AI decision-support in the initial management of orthopedic trauma: a systematic review}},
author = {Siddharth Jasti and Caden R. Moenning and Devon T. Foster and Albert Sirven and Christopher D. Varela and Vahini Srikakulapu and Joseph L. Whalen},
journal = {Journal of Orthopaedic Surgery and Research},
year = {2026},
month = jul,
doi = {10.1186/s13018-026-07107-5},
url = {https://doi.org/10.1186/s13018-026-07107-5},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - AI decision-support in the initial management of orthopedic trauma: a systematic review
AU - Siddharth Jasti
AU - Caden R. Moenning
AU - Devon T. Foster
AU - Albert Sirven
AU - Christopher D. Varela
AU - Vahini Srikakulapu
AU - Joseph L. Whalen
PY - 2026
DA - 2026/07/24
JO - Journal of Orthopaedic Surgery and Research
DO - 10.1186/s13018-026-07107-5
UR - https://doi.org/10.1186/s13018-026-07107-5
AB - Background: Early orthopedic trauma care requires rapid, high-stakes decision-making under conditions of physiologic instability and diagnostic uncertainty. Conventional clinical scores and guideline-based tools aim to standardize early management but often demonstrate limited patient-level accuracy and variable performance across care settings. Artificial intelligence (AI) and machine learning (ML) based decision-support tools have been proposed as adjuncts to support early trauma decisions, yet their scope and performance in orthopedic trauma remain uncharacterized. Methods: A PRISMA-guided systematic review was conducted of prediction-model and implementation studies published between January 2010 and October 2025 using MEDLINE (PubMed), Embase, and Cochrane CENTRAL. Eligible studies evaluated AI/ML-based tools designed to predict or support early orthopedic trauma decisions in the emergency department, trauma bay, or prehospital setting. Target decision domains included hemorrhage control, operative versus nonoperative management, limb salvage versus amputation, and level-of-care or interfacility triage. Data extraction focused on study design, clinical decision targets, model inputs, validation strategy and reported performance metrics. Results: From 361 identified records, 9 studies met inclusion criteria. Study populations, modeling approaches, and clinical targets were heterogeneous. Reported discriminatory performance was generally moderate to favorable, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.67 to 0.97 across decision domains. External validation was limited, and reporting of calibration and decision-analytic metrics was inconsistent. Imaging-adjacent AI tools enabled automated computed tomography (CT) based hematoma quantification for pelvic trauma and real-time CT auto-segmentation workflows for maxillofacial trauma (Dice ≈0.92–0.93) with substantial time reduction. However, the included studies did not directly evaluate downstream effects on clinician decisions or workflow throughput beyond segmentation tasks, or patient-centered outcomes. Conclusions: AI/ML-based decision-support tools for early orthopedic trauma management show strong technical feasibility and encouraging performance in existing validation studies, supporting their potential role as adjuncts to clinician judgment in early, high-stakes decisions. The current literature is dominated by retrospective evaluation and offers limited evidence on real-world impact. Next steps should prioritize prospective, workflow-integrated studies with standardized reporting of calibration and decision utility to determine how these tools can most effectively improve care delivery and patient outcomes in early orthopedic trauma. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13018-026-07107-5.
SN - 1749-799X
ER -