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The purpose of this review was to comprehensively explore the landscape of recently published literature on the applications of artificial intelligence (AI) in predicting individualized patient waiting times in an emergency department (ED) and identify pertinent considerations for practitioners and hospital decision-makers. ED overcrowding is being experienced by hospitals around the globe and has worsened in the post COVID-19 era. The negative patient and staff experiences and poor clinical outcomes from overcrowding are evident and necessitate solutions to address this ongoing problem. Hospitals providing ED waiting time estimates to patients and staff are becoming popular; however, the more common methods, such as using rolling averages, suffer from an inability to capture the nuanced relationships within an ED. Recent applications of AI and machine learning (ML) in healthcare raises the possibility of applying these techniques to individualized waiting time predictions in the ED; although, literature on the topic is sparse. A systematized search was conducted on November 10th, 2025, using the electronic databases CINAHL, EMBASE (OVID), Medline (OVID), PsychINFO, Web of Science, and PubMed. Articles were considered for review if written in English, peer-reviewed, published after 2014, and used AI techniques. Descriptive analysis was performed on the final extracted data to facilitate the identification of common themes across studies. Themes were inferred from the proportional usage among studies, of different data preparation, feature selection, and modeling strategies. The search identified 8613 citations that, after a rigorous screening process and critical appraisal, were narrowed down to 15 studies for final review. Most included studies were observational, using historical medical record data to compare modeling techniques or demonstrate a proof of concept. Studies commonly used one or more of ED queue-based, staff/resource-based, patient-based, and time-based feature categories. Incorporated AI methods included Random Forest, Linear Regression, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques, among several others. All forms of AI and ML outperformed traditional rolling average estimates used by hospitals. This review identified applications of AI in predicting individualized patient waiting times in the ED that outperform current waiting time estimate strategies. The use of nonlinear techniques, such as the Random Forest method, or incorporating queue-based feature categories, appeared to provide better performance in predictive estimates. Depending on the end user and modality in which the wait time estimate is conveyed, the importance of model selection is highlighted as a consideration to be made if overestimates or underestimates are preferred. • AI modeling techniques outperform traditional rolling average methods for predicting patient ED waiting times • Random forests and linear regression are the most common techniques used to predict patient ED wait times • The reviewed studies consistently identified queue-based features as significant predictors of wait times, or, as a set of features that can enrich the pool of predictors to improve the accuracy of predictions. • Literature on AI modeling for ED patient wait times is scarce and lacks feasibility implementation studies • Modeling over- or under- performance may be preferred depending on the modality and end-user in which the wait time estimate is used.
Gloyn et al. (Tue,) studied this question.