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.
Background
Heart rate variability (HRV) analysis powered by artificial intelligence (AI) offers a rapid, non-invasive, and objective approach for acute coronary syndrome (ACS) risk stratification in the emergency department (ED). The objective of this study was to evaluate the feasibility and impact of aiTriage™, an AI HRV-guided tool for chest pain triage, compared with standard care.
Methods
In this single-blinded randomized controlled trial, 560 ED patients with suspected ACS underwent 5- minute ECG monitoring for HRV analysis, which generated a 0–100 risk score and triage recommendations (high, medium, or low risk). Patients were randomized to standard care (control) or an HRV-guided protocol (intervention). Physicians in the control group were blinded to HRV results.
Results
Of 426 analysed patients (mean age 54 ± 13 years, 35% female, 16.2% prior MI), the HRV-guided protocol reduced hospital admissions (50.2% vs 61.1%; risk difference -10.9 percentage points, 95% CI: -20.1 to -1.8) and serial cardiac enzyme testing (32.1% vs 41.7%; risk difference -9.6 percentage points, 95% CI: -18.4 to -0.9) compared with standard care. Among discharged patients, the median ED length of stay was 20 minutes shorter in the intervention group (95% CI: -45 minutes to 3 minutes). The overall 30-day MACE rate was 9.5%, with no events among discharged patients.
Conclusion
A rapid AI HRV-guided risk stratification tool was feasible to deploy, and has potential to reduce serial cardiac enzyme testing, ED LOS and hospital admissions. An adequately powered RCT is needed to confirm these findings and assess clinical safety. This trial is registered at ClinicalTrials.gov (NCT07074808).
Objective This study integrated a machine learning–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-center, retrospective cohort study included all emergency department (ED) patients from a large tertiary hospital between January 1, 2018, and December 31, 2019. Using a reclassification framework, SERP was incorporated into PACS to derive two enhanced triage models. PACS+ model 1 down-triaged patients with low predicted 30-day mortality risk and up-triaged those with high risk. PACS+ model 2 up-triaged only high-risk patients, whereas 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.
Results The derivation cohort included 97,188 ED visits, and the test cohort included 97,212 ED visits. In the derivation set, the mean age of the patients was 58.97±18.41 years, and 47,993 (49.4%) were female. Overall, 19.9%, 57.4%, 22.5%, and 0.2% of patients were triaged to PACS categories P1–P4, respectively. The 30-day mortality rate was 2.8% in the derivation set and 2.7% in the test cohort. For 30-day mortality prediction, PACS+ model 1 (AUC, 0.828; 95% confidence interval [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 machine learning–based SERP with PACS improved 30-day mortality prediction in ED triage.
Outcomes in acute ischemic stroke (AIS) depend critically on rapid and accurate early diagnosis in the emergency department. Traditional prehospital tools and large vessel occlusion–focused scales facilitate triage but have limited capacity to distinguish ischemic from hemorrhagic stroke, a distinction essential for acute-phase treatment decisions. Recent advances include mobile stroke units equipped with computed tomography (CT), point-of-care laboratories, and telemedicine systems, as well as the emergence of biomarkers that enable field-based diagnosis and faster initiation of therapy. In-hospital imaging strategies incorporating CT, CT perfusion, and magnetic resonance imaging (MRI)-based tissue clocks have expanded eligibility for endovascular thrombectomy to include patients with large-core infarction or unclear-onset wake-up strokes. Prolonged cardiac monitoring and high-resolution vessel wall MRI have improved the detection of embolic sources and high-risk atherosclerotic plaques. Artificial intelligence now supports rapid imaging interpretation, workflow optimization, and treatment selection. Tenecteplase, a novel thrombolytic, provides a practical alternative to alteplase with comparable safety and efficacy, while post-thrombectomy management emphasizes individualized blood pressure control. In patients with minor stroke or high-risk transient ischemic attack, short-term dual antiplatelet therapy reduces early recurrence, and early initiation of lipid-lowering agents after AIS may stabilize vulnerable plaques and enhance vascular outcomes. Collectively, these innovations represent a shift toward integrated, time-sensitive, and precision-based AIS care spanning prehospital assessment, emergency department management, and post-reperfusion management.
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Current status and advances in comprehensive treatment of acute ischemic stroke in China: from evidence-based guidelines to clinical practice Ze-Ying Wang, Qin-Bao Zhang, Xiao-Ming Zheng Frontiers in Neurology.2026;[Epub] CrossRef
Objective This study aimed to develop and validate MEDIVAL (Medical Documentation Validation), a progressive chain-of-thought (CoT) evaluation framework for automated assessment of large language model (LLM)-generated emergency department documentation, designed to align with expert clinical judgment in acute care settings. Methods We designed a three-tier evaluation framework incorporating persona-based, error-enhanced, and insight-integrated strategies. The framework was tested across four LLMs (GPT-4o, GPT-4.1, Claude-3.5, Claude-3.7) on 33 emergency department records reviewed by four expert emergency physicians. Each model applied the three CoT strategies across five criteria: appropriateness, accuracy, structure/format, conciseness, and clinical validity. Model outputs were compared with expert ratings using Spearman correlation coefficients. Differences were analyzed with the Friedman test and Wilcoxon signed rank test with Bonferroni correction. Reproducibility was assessed through intraclass correlation coefficient (ICC) analysis. Results All models demonstrated stronger alignment with expert ratings as CoT complexity increased, with Claude-3.7 (r=0.712, P<0.001) and GPT-4o (r=0.702, P<0.001) showing the highest correlations under the insight-integrated strategy. GPT-4.1 showed the greatest relative improvement (43.3% increase, r=0.457 to r=0.655, P<0.001). Significant overall differences were observed across strategies (χ2 (2)=48.39, P<0.001), though the error-enhanced and insight-integrated approaches differed only modestly yet significantly (P=0.002). High reproducibility was confirmed (ICC >0.919), with Claude-3.5 achieving the most consistent results (ICC, 0.997–0.998). Conclusion MEDIVAL demonstrates that progressive CoT strategies systematically improve automated evaluation of emergency department documentation while maintaining excellent reproducibility. This framework offers a viable prescreening tool to reduce expert workload and support reliable artificial intelligence integration into emergency medicine workflows.
Objective This study aims to systematically review the ethical and legal discussions regarding the utilization of artificial intelligence (AI) for patient triage and resource allocation in emergency medicine, and to identify the current state of discussions, their limitations, and future research directions.
Methods A comprehensive literature search was conducted following scoping review methodology. Relevant literature published after January 2020 was searched in the Web of Science, Scopus, CINAHL, PubMed, and Cochrane Library databases. Based on a PCC (population, concept, and context) framework (emergency patients/medical staff; triage, resource allocation; and emergency medicine with AI application), a final selection of 27 articles was analyzed.
Results The selected literature raised various ethical and legal issues related to the introduction of AI triage systems and AI utilization in emergency medicine, including data privacy, algorithmic bias, automation dependency, accountability, and explainability. In response to these issues, human-centered design, implementation of explainable AI, establishment of regulatory frameworks, continuous verification and evaluation, and ensuring human-in-the-loop were discussed as major solutions. However, discussions on the risks of “persuasive AI” that could mislead users, ethical issues of generative AI, and social validation and patient and public involvement were found to be insufficient.
Conclusion Ethical and legal discussions regarding AI in emergency medicine are evolving toward seeking concrete solutions at technical, institutional, and relational dimensions. However, in-depth research on ethical challenges, such as reflecting the specificity of rapidly developing AI and the values of emergency medicine, is urgently required.
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The AI-IARA framework: How to cultivate human agency before artificial intelligence optimizes it a(ny)way Llewellyn E. van Zyl The Journal of Positive Psychology.2026; : 1. CrossRef
Sex-Consistent Performance of an AI-Enabled ECG for Acute Myocardial Infarction Hak Seung Lee, Sora Kang, Joon-myoung Kwon, Tae Gun Shin, Youngjoo Lee, Dong Hoon Kim, Sung Hyuk Choi, Hanjin Cho, Mi Jin Lee, Ki Young Jeong, Won Young Kim, Young Gi Min, Chul Han, Jae Chol Yoon, Eujene Jung, Woo Jeong Kim, Chiwon Ahn, Jeong Yeol Seo, Ta JACC: Advances.2026; 5(6): 102813. CrossRef
Objective We previously developed and validated an artificial intelligence-based electrocardiogram (ECG) analysis tool (ECG Buddy) in a Korean population. This study investigated the performance of this tool in a US population, specifically assessing the left ventricular (LV) dysfunction score and LV ejection fraction (LVEF)-ECG feature for predicting LVEF <40%. The study used N-terminal pro-B-type natriuretic peptide (NT-ProBNP) as a comparator.
Methods We identified emergency department (ED) visits from the MIMIC-IV dataset with information on LVEF <40% or ≥40% and matched 12-lead ECG data recorded within 48 hours of the ED visit. The performance of ECG Buddy’s LV dysfunction score and the LVEF-ECG feature was compared with those of NT-ProBNP using area under the receiver operating characteristic curve (AUC) analysis.
Results A total of 22,599 ED visits was analyzed. The LV dysfunction score had an AUC of 0.905 (95% confidence interval [CI], 0.899–0.910), with a sensitivity of 85.4% and specificity of 80.8%. The LVEF-ECG feature had an AUC of 0.908 (95% CI, 0.902–0.913), sensitivity of 83.5%, and specificity of 83.0%. NT-ProBNP had an AUC of 0.740 (95% CI, 0.727–0.752), with a sensitivity of 74.8% and specificity of 62.0%. The ECG-based predictors demonstrated superior diagnostic performance compared to NT-ProBNP (all P<0.001). In the sinus rhythm subgroup, the LV dysfunction score achieved an AUC of 0.913 and LVEF-ECG had an AUC of 0.917, both outperforming NT-ProBNP (AUC, 0.748; 95% CI, 0.732–0.763; all P<0.001).
Conclusion ECG Buddy demonstrated superior accuracy compared with NT-ProBNP in predicting LV systolic dysfunction, validating its utility in a US ED population.
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Screening for Left Ventricular Dysfunction in Sepsis Using a Smartphone ECG Analysis Application: A Multicenter Validation Study Yun Seong Park, Joonghee Kim, You Hwan Jo, Woon Yong Kwon, Kyoung Jun Song, Hui Jai Lee, Youngjin Cho, Ji Eun Hwang Journal of Korean Medical Science.2026;[Epub] CrossRef
Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease Yasmine Abbaoui, Alexis Nolin-Lapalme, Julianne Morisset, Ines El Adib, Philippe Genereux, Timothy J. Poterucha, Pierre Elias, Xioaxi Yao, Robert Avram Current Heart Failure Reports.2026;[Epub] CrossRef
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Objective Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department.
Methods Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board.
Discussion This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments.
Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.
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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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Over the past decades, the field of machine learning (ML) has made great strides in medicine. Despite the number of ML-inspired publications in the clinical arena, the results and implications are not readily accepted at the bedside. Although ML is very powerful in deciphering hidden patterns in complex critical care and emergency medicine data, various factors including data, feature generation, model design, performance assessment, and limited implementation could affect the utility of the research. In this short review, a series of current challenges of adopting ML models to clinical research will be discussed.
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