This scoping review explores the utilization of artificial intelligence in emergency nursing, assessing its impact, potential benefits, and the obstacles faced in its adoption. It covers the scope of AI from advanced triage protocols to continuous mo...
The American journal of emergency medicine
39731895
BACKGROUND: The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accur...
OBJECTIVE: Evaluate the accuracy and reliability of various generative artificial intelligence (AI) models (ChatGPT-3.5, ChatGPT-4.0, T5, Llama-2, Mistral-Large, and Claude-3 Opus) in predicting Emergency Severity Index (ESI) levels for pediatric eme...
World journal of emergency surgery : WJES
39716296
BACKGROUND: Acute abdominal pain (AAP) constitutes 5-10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with th...
Artificial intelligence (AI) has shown promise in revolutionizing medical triage, particularly in the context of the rising prevalence of kidney-related conditions with the aging global population. This study evaluates the utility of ChatGPT, a large...
BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these s...
Artificial intelligence (AI) tools can triage radiology scans to streamline the patient pathway and also relieve clinician workload. Validated AI tools can mitigate the delays in reporting scans by flagging time-sensitive and actionable findings. In ...
European journal of obstetrics, gynecology, and reproductive biology
39799741
OBJECTIVE: To investigate the potential of artificial intelligence (AI) in emergency medicine, focusing on its utility in triaging and managing acute gynecologic and obstetric emergencies.
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...
BACKGROUND: In Sweden with about 10 million inhabitants, there are about one million primary ambulance missions every year. Among them, around 10% are assessed by Emergency Medical Service (EMS) clinicians with the primary symptom of dyspnoea. The ri...