Artificial intelligence in emergency trauma perioperative management: applications, performance benchmarks, and translation barriers
4 affiliations
- 1Department of Emergency Medicine, Hainan General Hospital, Affiliated Hainan Hospital of Hainan Medical University, Haikou, Hainan, China
- 2Nursing Department, Changhai Hospital, Naval Medical University, Shanghai, China
- 3Nursing Department, Hainan Health Vocational College, Haikou, Hainan, China
- 4School of Nursing, Hainan Medical University, Haikou, Hainan, China
Abstract
This review examines the current status of artificial intelligence (AI) applications in the perioperative management of emergency trauma. Published studies suggest performance advantages of AI over traditional methods in prehospital triage, radiographic injury detection, massive transfusion prediction, and early warning of postoperative complications, although findings vary across study designs and validation settings. However, actual adoption in trauma care remains limited due to data heterogeneity, performance degradation in external validation, workflow integration challenges, lack of interpretability, and a scarcity of prospective interventional evidence. This review further argues that methodologically inflated benchmark performance constitutes an underrecognized barrier to adoption in its own right, and that bridging the implementation gap will likely require a shift from area under the receiver operating characteristic curve (AUC)-centered reporting toward reporting sensitivity at prespecified clinical thresholds and evaluating patient outcomes in prospective interventional trials.
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BibTeX
@article{yang2026artificial,
title = {{Artificial intelligence in emergency trauma perioperative management: applications, performance benchmarks, and translation barriers}},
author = {Lu Yang and Chunping Li and Xuren Wang and Yan Zeng and Hua Zhang},
journal = {Frontiers in Surgery},
year = {2026},
month = oct,
doi = {10.3389/fsurg.2026.1970304},
url = {https://doi.org/10.3389/fsurg.2026.1970304},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence in emergency trauma perioperative management: applications, performance benchmarks, and translation barriers
AU - Lu Yang
AU - Chunping Li
AU - Xuren Wang
AU - Yan Zeng
AU - Hua Zhang
PY - 2026
DA - 2026/10/26
JO - Frontiers in Surgery
DO - 10.3389/fsurg.2026.1970304
UR - https://doi.org/10.3389/fsurg.2026.1970304
AB - This review examines the current status of artificial intelligence (AI) applications in the perioperative management of emergency trauma. Published studies suggest performance advantages of AI over traditional methods in prehospital triage, radiographic injury detection, massive transfusion prediction, and early warning of postoperative complications, although findings vary across study designs and validation settings. However, actual adoption in trauma care remains limited due to data heterogeneity, performance degradation in external validation, workflow integration challenges, lack of interpretability, and a scarcity of prospective interventional evidence. This review further argues that methodologically inflated benchmark performance constitutes an underrecognized barrier to adoption in its own right, and that bridging the implementation gap will likely require a shift from area under the receiver operating characteristic curve (AUC)-centered reporting toward reporting sensitivity at prespecified clinical thresholds and evaluating patient outcomes in prospective interventional trials.
SN - 2296-875X
ER -