Table 1.

Selected studies (see additional file 4 for more detail)  

Article nr	The main aim of the study
[11]	To reduce cognitive load on clinicians by predicting the risk for admission
[15]	To reduce mortality by predicting the risk for (severe) sepsis in the ED
[16]	To help physicians by predicting the need for hospitalization
[17]	To help streamline crowded EDs by developing an AI tool that could remove the need for an expert emergency medicine physician during triage
[18]	To enhance ED triage systems by predicting mortality risk and risk for cardiac arrest
[19]	To prevent overcrowding of EDs by predicting future ED visits
[20]	To reduce ED morbidity and mortality by predicting the disposition of asthma and COPD exacerbation after triage
[21]	To increase physician satisfaction and reduce physician burnout by improving the efficiency and quality of structured data
[22]	To reduce/prevent overcrowding of EDs and improve patient care by predicting the need for hospitalization
[23]	To reduce ED morbidity and mortality costs by predicting risk for sepsis at triage and by implementing protocolized care
[24]	To reduce the length of stay (LOS) in ED by predicting clinical ordering at triage
[25]	To reduce/prevent overcrowding of EDs by predicting the risk for cardiac arrest in ED
[26]	To reduce ED morbidity and mortality and overcrowding of EDs by predicting triage levels for patients with suspected cardiovascular disease (CVD)
[27]	To cope with the increasing demand for clinical care in EDs by predicting septic shock at triage
[28]	To alleviate overburdened EDs and increase patients’ throughput by identifying patients’ need for a head CT scan at triage
[29]	To alleviate overburdened EDs by improving patient categorization by predicting ED mortality
[30]	To improve patients’ throughput in EDs by identifying severe thorax injury
[31]	To reduce overcrowding of EDs by predicting patient waiting times
[32]	To reduce overcrowding of EDs by developing an e-triage system
[33]	To improve patient outcomes and reduce adverse effects by identifying patients at risk for acute kidney failure
[34]	To prevent adverse outcomes by predicting/identifying the geriatric need for hospitalization
[35]	To improve patient outcomes by identifying scaphoid fractures
[36]	To improve patient outcomes by predicting patient waiting times
[37]	To cope with overcrowding of EDs through predicting critical care and hospitalization outcomes at triage
[38]	To improve patient outcomes by linking prehospital records to hospital records
[39]	To safely reduce hospital admissions by predicting risk for 30-day adverse severe events
[40]	To improve patient outcomes and enhance physician ability by identifying ECG outcomes
[41]	To increase patient throughput in crowded EDs by predicting patient disposition during triage
[42]	To reduce diagnostic errors (and costs & overutilization of resources) by predicting/identifying urinary tract infections (UTIs) early
[43]	To improve healthcare delivery by predicting future hospital demand
[44]	To improve healthcare provider wellbeing and preserve patient safety by predicting clinician workload
[45]	To cope with overcrowding of EDs by predicting adverse clinical outcomes at tirage
[46]	To improve patient outcomes by identifying septic shock at an early stage
[47]	To reduce diagnostic errors and excess costs by predicting and identifying severe cardiac events