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.

This study developed and validated a digital twin of patient flow and waiting list dynamics in a Sleep‐Disordered Breathing Unit, based on a discrete‐event simulation (DES) model in the context of growing diagnostic demand mainly driven by obstructive sleep apnoea. The stochastic model, implemented in MATLAB, reproduces six care stages (e‐Consultation, face‐to‐face consultation, overnight pulse oximetry, respiratory polygraphy, polysomnography and follow‐up consultation), using operational data from year 2024 for parameterisation and data from year 2025 for correlation and validation. Key performance indicators included mean waiting time and mean queue length per stage, with model fidelity assessed via Mean Absolute Error, Root Mean Squared Error and Relative Percent Difference (RPD), both by stage and globally across the full patient pathway. The model accurately reproduced the aggregate system workload, with a weighted RPD of 3.6% for mean queue length and 34.6% for mean waiting time. Respiratory polygraphy showed the best agreement in terms of service load (RPD 2.7%), whereas the follow‐up consultation exhibited the largest discrepancies in waiting times (RPD 73.7%), likely related to prioritisation rules and organisational variability not explicitly modelled. Overall, this digital twin provides an operationally useful representation to support ‘what‐if’ analyses of organisational scenarios aimed at reducing diagnostic delays, representing one of the first validated applications of DES model in a dedicated Sleep‐Disordered Breathing Unit and provides a basis for future applications incorporating urgency stratification and prospective analysis.

Background and Objectives: Non-invasive mechanical ventilation is the first-line treatment for managing acute respiratory failure. However, patient variability and complex pulmonary mechanics complicate therapy adjustments, frequently leading to ventilator-induced lung injuries. This study aims to propose and define a simulation platform and decision support prototype, named VENTILA2, to optimize pressure-controlled ventilation strategies. Methods: The system integrates a bicompartmental series model of the respiratory system incorporating severity-stratified physiological profiles of chronic obstructive pulmonary disease and acute respiratory distress syndrome, and it is coupled with a Mamdani fuzzy inference system. This architecture maps inspiratory time adjustments based on pressure errors and their derivatives across predefined clinical profiles within a scenario-based feedforward parameter-mapping framework. Results: Evaluated through quantitative operational verification across all profiles and proof-of-concept case studies, the platform successfully recreates complex clinical scenarios, accurately simulating phenomena such as accelerated lung emptying in severe acute respiratory distress syndrome and air trapping in moderate chronic obstructive pulmonary disease. Conclusions: VENTILA2 provides a controlled simulation environment for evaluating pathology-specific ventilatory configurations across simulated profiles prior to clinical implementation, though it remains an early-stage prototype whose clinical effectiveness, safety, and robustness remain to be rigorously evaluated.

This study developed and validated a digital twin of patient flow and waiting list dynamics in a Sleep‑Disordered Breathing Unit, based on a discrete‑event simulation (DES) model in the context of growing diagnostic demand mainly driven by obstructive sleep apnoea. The stochastic model, implemented in MATLAB, reproduces six care stages (e-Consultation, face‑to‑face consultation, overnight pulse oximetry, respiratory polygraphy, polysomnography and follow‑up consultation), using operational data from year 2024 for parameterisation and data from year 2025 for correlation and validation. Key performance indicators included mean waiting time and mean queue length per stage, with model fidelity assessed via Mean Absolute Error, Root Mean Squared Error and Relative Percent Difference (RPD), both by stage and globally across the full patient pathway. The model accurately reproduced the aggregate system workload, with a weighted RPD of 3.6% for mean queue length and 34.6% for mean waiting time. Respiratory polygraphy showed the best agreement in terms of service load (RPD 2.7%), whereas the follow‑up consultation exhibited the largest discrepancies in waiting times (RPD 73.7%), likely related to prioritisation rules and organisational variability not explicitly modelled. Overall, this digital twin provides an operationally useful representation to support “what‑if” analyses of organisational scenarios aimed at reducing diagnostic delays, representing one of the first validated applications of DES model in a dedicated Sleep-Disordered Breathing Unit and provides a basis for future applications incorporating urgency stratification and prospective analysis.