Combined Applications of Artificial Intelligence and Simulation for Healthcare Process Optimization: A Systematic Review

Abstract

BACKGROUND: Healthcare systems face significant challenges due to waiting times, resource shortages, and increasing demand for services. The combination of Artificial Intelligence (AI) and simulation is emerging as a promising solution to optimise healthcare processes, although their joint application remains limited. This systematic review analyses current methodological approaches that integrate both technologies to enhance healthcare management. METHODS: A systematic search was conducted in PubMed and IEEE Xplore for articles published between 2014 and 2025, following PRISMA guidelines. The search strategy included terms related to AI, simulation, and healthcare management, and was supplemented by a "snowball" search. Original studies describing combined applications of AI and simulation in healthcare processes were included. RESULTS: Out of 2506 records identified, 22 studies were selected for final analysis, most of which were published between 2021 and 2025, indicating growing interest in the field. The studies show that integrating AI and simulation has the potential to improve the efficiency of healthcare management, optimise resource allocation, and reduce waiting times, particularly in areas such as emergency departments and clinical pathways. Supervised learning algorithms, discrete event simulation (DES), and agent-based systems (ABS) were the most commonly used approaches. CONCLUSIONS: The combination of AI and simulation is an emerging field with great potential to revolutionise the management of healthcare processes. However, effective implementation requires overcoming technological, standardisation, and data integration barriers, as well as expanding its application to more hospital departments to maximise its impact.

Ähnliche Arbeiten