Background <p>Prehospital emergency medicine is a high-risk environment where time-critical decisions must be made under adverse conditions. Artificial Intelligence (AI) holds the potential to enhance patient care through data-driven decision support. This narrative review analyses current and emerging AI applications within emergency medical services and evaluates their impact on the quality dimensions defined by Donabedian (structural, process, and outcome quality). It is intended for paramedics and clinicians in the prehospital setting and aims to encourage further engagement with AI in emergency medical services to reduce cognitive load and ultimately improve patient outcomes.</p> Methods <p>A review of international literature was conducted to identify AI applications in emergency medical services. The analysis was structured according to typical operational phases: emergency call interrogation, dispatch and resource allocation, operational-tactical applications, and clinical applications. Additionally, hypothetical scenarios and illustrative case studies were developed to demonstrate operational pathways and practical utility. Evaluation was guided by established healthcare quality indicators and conducted within a narrative, qualitatively integrative framework, reflecting the methodological heterogeneity of the available evidence.</p> Results <p>AI-based technologies demonstrate promising applications across all operational phases, but implementation is still mostly limited to pilot projects and local solutions. In emergency call interrogation, AI-driven speech recognition can increase the detection rate of out-of-hospital cardiac arrest by 43% and reduce detection time by 25%. AI-supported dispatch systems can improve on-time performance for highly urgent calls by 0.77% points through dynamic, data-driven resource reassignment [<CitationRef CitationID="CR1">1</CitationRef>]. Clinical applications include AI-based ECG interpretation with 99.2% accuracy and a reporting time of 37.2 ± 11.3 s, as well as AI-supported ultrasonography that optimises image acquisition for inexperienced users. Operational-tactical systems enable real-time transmission and automated analysis of prehospital data with 90% automated detection accuracy, supporting triage decisions and destination hospital selection.</p> Conclusions <p>AI technologies have the potential to enhance all quality dimensions of prehospital emergency medicine by integrating heterogeneous data streams, reducing cognitive load, and supporting time-critical decision-making. However, their use remains constrained by deficits in training, regulation, financing, interoperability, and governance, as well as by unresolved ethical and safety concerns such as algorithmic bias, model drift, and cybersecurity vulnerabilities. Evidence-based, stepwise implementation involving EMS professionals, robust evaluation, and clearly defined fallback structures is required to improve patient safety and maintain professional autonomy. In this context, AI should be understood as a supportive tool embedded within resilient systems, not as a substitute for clinical expertise.</p> Clinical trial number <p>Not applicable.</p>