Indian journal of orthopaedics
March 19, 2026 · via Europe PMC
Artificial Intelligence and Decision Support in Orthopaedic Emergencies: A Review.
Vaishya R, Saiyed A, Vaish A, Botchu R, Iyengar KP, Arjuman A.
AI summary — for doctors, generated from the abstract only
AI shows promise for fracture detection and triage in orthopaedic emergencies but needs external validation and responsible integration.
What was studied
A narrative review of AI applications for fracture detection, triage, decision support and outcome prediction through February 2026.
Key findings
- Deep learning matched or exceeded clinicians in fracture detection in some studies
- AI triage outperformed conventional scores in some studies
- Challenges include bias, interpretability and regulation
Clinical relevance
AI could help where specialists are scarce.
Who should care
Limitations
A narrative review with mostly retrospective evidence.
Use AI as an adjunct, not a replacement, for clinical judgment.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
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
DOI
10.1007/s43465-026-01760-y
PMID
42824522
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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