Artificial Intelligence in Emergency Medical Services: A Systematic Review Study

Background and aims: With the rapid advancement of emergency medical services (EMS),the integration of artificial intelligence (AI) technologies has become increasingly prevalent inpre-hospital emergency care. In view of this trend, the present study aims to provide a comprehensiveoverview of AI applications in EMS on a global scale. The purpose of this study is toconsolidate the current state of knowledge on the subject and provide insights into potential futuredirections for the use of AI in pre-hospital emergency care.Method: This study utilized the Prisma method to evaluate the quality of systematic reviewstudies. The search was conducted using relevant keywords such as artificial intelligence, machinelearning, and emergency medical services in English databases such as WoS, PubMed,and Scopus, as well as other grey resource websites, without imposing any temporal restrictions.The results of the search were reviewed separately by two researchers based on the article’s title,summary, and keywords. Subsequently, the full text of relevant articles was screened and assessedaccording to predetermined inclusion and exclusion criteria. The included studies were then synthesizedand analyzed to present a comprehensive review of the present state of systematic reviewstudies pertaining to artificial intelligence, machine learning, and emergency medical services.Results: Out of the initial pool of ۸۵۴ studies, ۲۵ studies were carefully selected for full text evaluationand data extraction after excluding duplicates and irrelevant studies. The results of thesestudies indicate that AI is being widely implemented in the pre-hospital emergency medical systemfor a variety of purposes. These applications include the analysis of emergency phone callsto enable early diagnosis of certain diseases, as well as the use of smart cameras in public placesto detect critical medical emergencies, and access to clients and ensuring intelligent recording ofemergency reports. Furthermore, researchers have developed web-based and Geographic InformationSystem -enabled smart software to facilitate emergency response management. Additionally,traffic systems can be connected to emergency care systems to streamline response times inthe event of an emergency, and the development of smart electronic bracelets can record informationand track clients during mass casualty incidents. Furthermore, the creation of intelligent softwarefor patient triage, location management, and allocation of patients to personnel during masscasualty incidents, can help ensure a more efficient and effective response to emergencies. Developmentand utilization of intelligent robot ambulance caregivers, predicting the type, amount, andlocation of future missions and identifying the healthcare needs of patients, training and simulatedexercises for emergency medical personnel, and online telemedicine system between emergencymedical services and hospitals were the other implications of AI in prehospital management.Conclusion: In conclusion, the findings of this study demonstrate the critical and pervasive roleof EMS within the healthcare system. Advances in artificial intelligence have the potential to revolutionizenumerous EMS processes including accident forecasting, decision-making, planning,patient triage, timely treatment, and efficient patient transfer. Implementation of AI technology inEMS holds significant promise for improving patient outcomes and enhancing emergency medicalcare.

کلیدواژه ها:

Artificial intelligence ، Emergency Medical Services ، Pre-hospital ، Emergencies ، Machin learning ، Health