Using AI to Improve Emergency Care: A Detailed Study of Benefits, Challenges and What's Next

Abstract

Emergency Medical Services (EMS), while vital for rapid response to life-threatening emergencies, face challenges such as resource constraints, time-sensitive decision-making, and limited access to specialized expertise. Artificial Intelligence (AI) has emerged as a transformative tool in EMS, offering potential improvements in diagnostics, resource allocation, and emergency response through its ability to analyze medical data, recognize patterns, and support decision-making. This systematic review, adhering to PRISMA guidelines and utilizing the Joanna Briggs Institute (JBI) Critical Appraisal Tools, evaluated 12 high-quality studies (selected from 571 screened papers across PubMed, Scopus, Web of Science, and Google Scholar) to assess AI's impact on EMS. Key findings revealed that AI-based clinical decision support systems significantly reduced hospital readmissions by enabling accurate risk prediction and timely interventions, while AI-enhanced dispatch tools, such as automatic speech recognition software, improved stroke detection and response times. Additionally, AI-assisted protocols in critical ambulance services demonstrated reliability in high-stakes environments, fostering trust among emergency medical technicians. However, challenges including healthcare providers' adaptation to AI tools and the need for standardized safety protocols highlight gaps in implementation. The variability in AI adoption across EMS settings limits broad conclusions, underscoring the necessity for future research to include diverse environments, particularly in low- and middle-income countries, to evaluate AI's adaptability. Developing universal benchmarks for assessing AI technologies in EMS, alongside addressing ethical concerns like algorithmic transparency and equitable resource distribution, will be critical to optimizing AI's role in enhancing emergency care outcomes globally.

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