Out-of-hospital cardiac arrest (OHCA) remains a major cause of death and long-term disability worldwide. Survival hinges on multiple factors, importantly whether the initial rhythm is shockable and how rapidly defibrillation is delivered; automated external defibrillators (AEDs) are therefore pivotal. However, AED availability may vary widely between regions, and there is often a mismatch between AED placement and OHCA locations. Geospatial optimization offers a data-driven strategy to improve AED coverage. Although international organizations provide recommendations for the deployment of automated external defibrillators (AEDs), these guideline-based strategies often leave critical gaps in coverage. Mathematical optimization has therefore emerged as a promising complement to existing recommendations, allowing AED placement to be tailored to real-world patterns of OHCA incidence. The purpose of this study was to examine which mathematical models have been successfully applied to AED network design and to investigate the variables that could be used to optimize AED networks. There is consistent evidence that strategic placement of AEDs significantly improves OHCA coverage, potentially surpassing coverage achieved through guideline-based approaches.Presented by Rok Miklič.