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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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AI decision-support in the initial management of orthopedic trauma: a systematic review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Siddharth Jasti,
  • Caden R. Moenning,
  • Devon T. Foster,
  • Albert Sirven,
  • Christopher D. Varela,
  • Vahini Srikakulapu,
  • Joseph L. Whalen

摘要

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.