AI-Based Decision Support Systems for Emergency Medical Services

Abstract

The increasing demand for efficient and accurate emergency medical services (EMS) has driven the adoption of artificial intelligence (AI)-based decision support systems (DSS). These systems leverage machine learning algorithms, natural language processing, and real-time data analytics to enhance clinical decision-making and operational efficiency in prehospital and emergency care settings. AI-powered DSS can assist EMS personnel in diagnosing critical conditions, predicting patient outcomes, optimizing ambulance dispatch, and improving resource allocation. They also facilitate faster and more accurate treatment decisions, leading to better patient outcomes. Despite their significant potential, challenges remain, including data quality issues, integration complexities, and the need for clinician trust in AI recommendations. This paper explores the current landscape of AI-based DSS in EMS, highlighting recent advancements, case studies, and the challenges associated with their implementation. Future research directions focus on improving the interpretability, scalability, and ethical considerations of these systems to ensure widespread adoption and improved emergency care delivery.