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Background <p>Orthopaedic emergencies require rapid, accurate clinical decisions to prevent limb- and life-threatening complications. Increasing patient volume, limited specialist availability, and diagnostic complexity contributes to delays and errors in emergency care. Artificial intelligence (AI) encompasses machine learning (ML) and deep learning (DL) and has emerged as a promising tool to augment decision-making in orthopaedic emergencies.</p> Purpose <p>To review current applications, evidence, limitations, and future directions of AI-based decision-support systems in orthopaedic emergency care, with particular relevance to low- and middle-income healthcare settings.</p> Methods <p>A comprehensive electronic search was performed in major academic databases. This included PubMed, Embase, Scopus, and Web of Science to identify all relevant literature published in English from inception up to February 2026, focusing on AI applications in orthopaedic trauma, emergency imaging, triage, clinical decision support, and outcome prediction. Emphasis was placed on clinically validated studies, systematic reviews, and emerging technologies relevant to emergency orthopaedics.</p> Results <p>AI applications in orthopaedic emergencies primarily included automated fracture detection, injury classification, triage optimisation, clinical decision support, and complication prediction. DL models have demonstrated diagnostic accuracy comparable to or exceeding that of expert clinicians in fracture detection. AI-assisted triage systems reportedly outperformed conventional scoring tools in predicting severity and admission needed based on the studies included for this review. Clinical decision-support tools improved the completeness and consistency of emergency management recommendations based on hospital-based and practice-oriented studies. The reported challenges included those related to data bias, interpretability, clinical integration, ethical concerns, and regulatory oversight.</p> Conclusions <p>AI has substantial potential to enhance diagnostic accuracy, efficiency, and decision-making in orthopaedic emergencies. Responsible adoption requires robust external validation, clinician education, explainable models, and integration into existing workflows. When appropriately implemented, AI can serve as a valuable adjunct to, rather than a replacement for, clinical expertise in orthopaedic emergency care.</p> Graphical Abstract <p></p>

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Artificial Intelligence and Decision Support in Orthopaedic Emergencies: A Review

  • 期刊论文

  • 发表日期：

    2026年3月19日

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  • Raju Vaishya,
  • Ayaz Saiyed,
  • Abhishek Vaish,
  • Rajesh Botchu,
  • Kartikeyan P. Iyengar,
  • Albina Arjuman

摘要

Background

Orthopaedic emergencies require rapid, accurate clinical decisions to prevent limb- and life-threatening complications. Increasing patient volume, limited specialist availability, and diagnostic complexity contributes to delays and errors in emergency care. Artificial intelligence (AI) encompasses machine learning (ML) and deep learning (DL) and has emerged as a promising tool to augment decision-making in orthopaedic emergencies.

Purpose

To review current applications, evidence, limitations, and future directions of AI-based decision-support systems in orthopaedic emergency care, with particular relevance to low- and middle-income healthcare settings.

Methods

A comprehensive electronic search was performed in major academic databases. This included PubMed, Embase, Scopus, and Web of Science to identify all relevant literature published in English from inception up to February 2026, focusing on AI applications in orthopaedic trauma, emergency imaging, triage, clinical decision support, and outcome prediction. Emphasis was placed on clinically validated studies, systematic reviews, and emerging technologies relevant to emergency orthopaedics.

Results

AI applications in orthopaedic emergencies primarily included automated fracture detection, injury classification, triage optimisation, clinical decision support, and complication prediction. DL models have demonstrated diagnostic accuracy comparable to or exceeding that of expert clinicians in fracture detection. AI-assisted triage systems reportedly outperformed conventional scoring tools in predicting severity and admission needed based on the studies included for this review. Clinical decision-support tools improved the completeness and consistency of emergency management recommendations based on hospital-based and practice-oriented studies. The reported challenges included those related to data bias, interpretability, clinical integration, ethical concerns, and regulatory oversight.

Conclusions

AI has substantial potential to enhance diagnostic accuracy, efficiency, and decision-making in orthopaedic emergencies. Responsible adoption requires robust external validation, clinician education, explainable models, and integration into existing workflows. When appropriately implemented, AI can serve as a valuable adjunct to, rather than a replacement for, clinical expertise in orthopaedic emergency care.

Graphical Abstract