Objective Emergency department (ED) triage determines patient prioritization, early risk recognition, and allocation of limited resources. Artificial intelligence (AI) has been explored to support triagerelated decision-making, but the evidence remains heterogeneous. This scoping review aimed to map applications of AI-assisted triage in EDs and summarize reported outcomes, safety, equity, and implementation challenges.
Methods This scoping review followed Arksey and O’Malley’s framework. Scopus, PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and manual searching were used. Original empirical studies published in English between 2015 and 2026 were included if they evaluated AI tools supporting ED triage, risk stratification, resource prediction, or patient-flow decision-making.
Results Of 1,865 records identified, 27 studies met the inclusion criteria. Included studies used machine learning, deep learning, natural language processing (NLP), artificial neural networks, interpretable machine learning, AI-informed decision-support systems, and large language models (LLMs). AI was applied to acuity classification, admission prediction, intensive care unit (ICU) admission prediction, mortality prediction, sepsis detection, waiting-time estimation, and patient-flow optimization. Conventional machine learning and NLP models generally reported promising predictive performance, particularly when structured triage variables were combined with unstructured clinical text. However, fewer studies evaluated prospective validation, workflow integration, measurable clinical and operational impact, equity, or post-deployment monitoring. Evidence on LLMs remains preliminary, with concerns about undertriage, inconsistency, hallucination, local adaptability, and the need for supervised use.
Conclusion AI-assisted triage may support ED decision-making and patient-flow management. Future implementation should prioritize supervised human–AI collaboration, prospective validation, explainability, fairness assessment, clinician training, workflow integration, and continuous monitoring.
OBJECTIVE This study integrates a machine learning (ML) based Score for Emergency Risk Prediction (SERP), developed using objective mortality endpoints with the Patient Acuity Category Scale (PACS) and evaluated its effectiveness in clinical use.
METHODS This single-centre, retrospective cohort study included all ED patients from a large tertiary hospital between 1 January 2018 and 31 December 2019. Using a reclassification framework, SERP was incorporated into PACS to derive two enhanced triage models. PACS+ model 1 downtriaged patients with low predicted 30-day mortality risk and up-triaged those with high risk. PACS+ model 2 up-triaged only high-risk patients, while low-risk patients retained their original category. Predictive performance in the test cohort was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).
RESULTS The derivation cohort included 97,188 ED visits, and test cohort included 97,212 ED visits. In the derivation set, the mean (SD) age of patients was 58.97 (18.41) years old and 47,993 (49.4%) were females. Of all patients, 19.9%, 57.5%, 22.5%, and 0.2% were triaged to PACS categories 1–4 respectively. The 30-day mortality rate in the derivation set was 2.8% and 2.7% in the validation cohort. For 30-day mortality prediction, PACS+ model 1 (AUC 0.828 [95% CI 0.820-0.836]) and PACS+ model 2 (AUC 0.812 [95% CI 0.805-0.818]) outperformed PACS (AUC 0.722 [95% CI 0.714-0.729]). PACS+ model 1 consistently achieved greater net benefit across the range of clinical thresholds.
CONCLUSION Integrating ML-based SERP with PACS improved 30-day mortality prediction in ED triage.
Artificial intelligence (AI) and machine learning (ML) have potential to revolutionize emergency medical care by enhancing triage systems, improving diagnostic accuracy, refining prognostication, and optimizing various aspects of clinical care. However, as clinicians often lack AI expertise, they might perceive AI as a “black box,” leading to trust issues. To address this, “explainable AI,” which teaches AI functionalities to end-users, is important. This review presents the definitions, importance, and role of explainable AI, as well as potential challenges in emergency medicine. First, we introduce the terms explainability, interpretability, and transparency of AI models. These terms sound similar but have different roles in discussion of AI. Second, we indicate that explainable AI is required in clinical settings for reasons of justification, control, improvement, and discovery and provide examples. Third, we describe three major categories of explainability: pre-modeling explainability, interpretable models, and post-modeling explainability and present examples (especially for post-modeling explainability), such as visualization, simplification, text justification, and feature relevance. Last, we show the challenges of implementing AI and ML models in clinical settings and highlight the importance of collaboration between clinicians, developers, and researchers. This paper summarizes the concept of “explainable AI” for emergency medicine clinicians. This review may help clinicians understand explainable AI in emergency contexts.
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Objective The aim of this study was to determine the feasibility of using machine learning to establish the need for preclinical airway management for injured patients based on a standardized emergency dataset.
Methods A registry-based, retrospective analysis was conducted of adult trauma patients who were treated by physician-staffed emergency medical services in southwestern Germany between 2018 and 2020. The primary outcome was to assess the feasibility of using the random forest (RF) and Naive Bayes (NB) machine learning algorithms to predict the need for preclinical airway management. The secondary outcome was to use a principal component analysis to determine the attributes that can be used and advanced for future model development.
Results In total, 25,556 adults with multiple injuries were identified, including 1,451 patients (5.7%) who required airway management. Key attributes were auscultation, injury pattern, oxygen therapy, thoracic drainage, noninvasive ventilation, catecholamines, pelvic sling, colloid infusion, initial vital signs, preemergency status, and shock index. The area under the receiver operating characteristics curve was between 0.96 (RF; 95% confidence interval [CI], 0.96–0.97) and 0.93 (NB; 95% CI, 0.92–0.93; P<0.01). For the prediction of airway management, RF yielded a higher precision-recall area than NB (0.83 [95% CI, 0.8–0.85] vs. 0.66 [95% CI, 0.61–0.72], respectively; P<0.01).
Conclusion To predict the need for preclinical airway management in injured patients, attributes that are commonly recorded in standardized datasets can be used with machine learning. In future models, the RF algorithm could be used because it has robust prediction accuracy.
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Clin Exp Emerg Med 2022;9(4):345-353. Published online September 21, 2022
Objective Falls are one of the most frequently occurring adverse events among hospitalized patients. The Morse Fall Scale, which has been widely used for fall risk assessment, has the two limitations of low specificity and difficulty in practical implementation. The aim of this study was to develop and validate an interpretable machine learning model for prediction of falls to be integrated in an electronic medical record (EMR) system.
Methods This was a retrospective study involving a tertiary teaching hospital in Seoul, Korea. Based on the literature, 83 known predictors were grouped into seven categories. Interpretable fall event prediction models were developed using multiple machine learning models including gradient boosting and Shapley values.
Results Overall, 191,778 cases with 272 fall events (0.1%) were included in the analysis. With the validation cohort of 2020, the area under the receiver operating curve (AUROC) of the gradient boosting model was 0.817 (95% confidence interval [CI], 0.720–0.904), better performance than random forest (AUROC, 0.801; 95% CI, 0.708–0.890), logistic regression (AUROC, 0.802; 95% CI, 0.721–0.878), artificial neural net (AUROC, 0.736; 95% CI, 0.650–0.821), and conventional Morse fall score (AUROC, 0.652; 95% CI, 0.570–0.715). The model’s interpretability was enhanced at both the population and patient levels. The algorithm was later integrated into the current EMR system.
Conclusion We developed an interpretable machine learning prediction model for inpatient fall events using EMR integration formats.
Citations
Citations to this article as recorded by
Development and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study Xiyao Yang, Juan Ren, Dan Su, Manzhen Bao, Miao Zhang, Xiaoming Chen, Yanhua Li, Zonggui Wang, Xiujing Dai, Zengzeng Wei, Shuiyu Zhang, Yuxin Zhang, Juan Li, Xiaolin Li, Junjin Xu, Nan Mo JMIR Aging.2026; 9: e80602. CrossRef
Cognitive readiness of nurses regarding artificial intelligence predictions: understanding through the dual lens of verbatim and gist knowledge Insook Cho, Soyun Shim, Hyunchul Park JAMIA Open.2026;[Epub] CrossRef
Prediction of inpatient falls and key predictors using machine learning applied to electronic health records: a retrospective cohort study in a tertiary hospital in Türkiye Veysel Karani Baris, Burcu Hudaverdi BMJ Open.2026; 16(5): e113384. CrossRef
Relative contributions of modifiable risk factors to injurious fall prediction in older adults: A predictive modelling study Tewodros Yosef, Julie A Pasco, Monica C Tembo, Kara B Anderson, Kara L Holloway-Kew Archives of Gerontology and Geriatrics.2026; 150: 106333. CrossRef
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Machine-learning-based Fall-prediction Model for Inpatients in Military Hospitals YunJung Choi, WooJin Lee, Juyeon Baek CIN: Computers, Informatics, Nursing.2026;[Epub] CrossRef
Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review Francesco De Micco, Gianmarco Di Palma, Davide Ferorelli, Anna De Benedictis, Luca Tomassini, Vittoradolfo Tambone, Mariano Cingolani, Roberto Scendoni Frontiers in Medicine.2025;[Epub] CrossRef
Machine learning-based prediction models for falls in hospitalized patients: A systematic review and meta-analysis Ronggui Xie, Le Shao, Jingru Pei, Yuyan Shi, Mingming Tang, Xueqin Sun, Guiyu Deng, Hong Zhao Geriatric Nursing.2025; 63: 487. CrossRef
Digital Healthcare Approaches for Fall Detection and Prediction in Older Adults: A Systematic Review of Evidence from Hospital and Long-Term Care Settings Aijin Lee, Haneul Lee, Seon-Heui Lee Medicina.2025; 61(11): 1926. CrossRef
An inpatient fall risk assessment tool: Application of machine learning models on intrinsic and extrinsic risk factors Sonia Jahangiri, Masoud Abdollahi, Rasika Patil, Ehsan Rashedi, Nasibeh Azadeh-Fard Machine Learning with Applications.2024; 15: 100519. CrossRef
The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review Jessica Caterson, Alexandra Lewin, Elizabeth Williamson DIGITAL HEALTH.2024;[Epub] CrossRef
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