applications of machine learning models in optimizing triage accuracy and predicting patient outcomes in emergency care: a narrative review of current evidence

Artificial intelligence plays a central role in patient triage by enhancing the accuracy and efficiency of ranking care, allowing rapid identification of critically ill patients, reducing under- and over-triaging, and enhancing resource distribution in clinical settings, which eventually improves patient outcomes and reduces delay times. this study aimed to assess and summarize the current evidence on how artificial intelligence (ai), particularly machine learning (ml) models, are used to improve the accuracy of triage and predict patient outcomes in emergency departments (eds). a widespread search was conducted across three major scientific databases, targeting studies published between 2023 and 2024. the search strategy combined keywords related to ai, ml, ed, triage, and patient outcomes. the studies evaluated a broad range of patient variables, including demographic characteristics (age, gender, ethnicity, socioeconomic status), vital signs (heart rate, respiratory rate, blood pressure, oxygen saturation, body temperature), medical history, symptoms, laboratory results, imaging data (ct scans, ecgs, slit lamp images), and emergency visit details. ml and ai models generally enhanced triage accuracy, with some achieving high performance metrics (e.g., 91% auc and 70% f1 score using histogram-based gradient boosting classifiers) and effectively predicting critical outcomes, such as intubation need, icu admission, in-hospital cardiac arrest, and vasopressor administration. chatgpt showed promise in specialized triage contexts, such as metastatic prostate cancer; however, it had notable under-triage rates in high-acuity groups. ai-assisted imaging significantly improved sensitivity in detecting conditions, such as inferior vena cava embolism, without loss of specificity. in emergency eye care, ai combined with ocular imaging was beneficial but limited to that specialty. overall, ai and ml models demonstrated positive impacts on triage efficacy and patient outcome prediction across diverse emergency care settings. these improvements translate into better identification of critically ill patients and more efficient use of ed resources.