## 1 Introduction
Furthermore, one of the most significant advantages of the ANN-PSO approach is its ability to adapt to real-time data streams, a crucial feature in telemedicine environments where patient arrival patterns and service durations can fluctuate unpredictably. Unlike traditional queuing models that rely on historical data, ANN-PSO continuously updates its predictive model using the latest available information, which makes it particularly effective for large-scale telehealth operations [14]. The scalability of ANN-PSO further enhances its applicability in telemedicine. Whether deployed in small clinics or large-scale hospital networks, ANN-PSO can optimize telemedicine service delivery by fine-tuning queue management parameters to meet specific operational needs [15]. Moreover, through leveraging PSO's ability to explore and exploit different weight configurations, ANN-PSO ensures that telemedicine platforms operate with maximum efficiency [16]. Additionally, PSO's stochastic search mechanism allows ANN-PSO models to avoid the common pitfalls of traditional optimization algorithms, such as premature convergence to suboptimal solutions. This ensures that ANN-PSO maintains a high level of predictive accuracy even in complex, multi-modal optimization landscapes. Studies have shown that PSO-driven ANN models exhibit faster convergence rates compared to traditional backpropagation-based models, which make them ideal for real-time decision-making in healthcare applications [5]. In view of this, the hybrid ANN-PSO method signifies notable progress in telemedicine queue optimization. Integrating the forecasting abilities of ANNs with the global search effectiveness of PSO, this system allows for the precise, adaptive and efficient management of telemedicine queues. The capacity of ANN-PSO to perpetually enhance its predictions using real-time data guarantees optimal resource distribution, shorter patient waiting times and better service delivery in various healthcare environments. With the ongoing growth of telemedicine, the incorporation of ANN-PSO models into queue management systems is likely to significantly improve healthcare accessibility and efficiency [17]. ANNs enhanced through PSO have shown substantial advancements in healthcare processes, especially in managing queues and optimizing patient services. Several studies have offered empirical proof of the effectiveness of ANN-PSO models in improving queue prediction precision, resource distribution and system efficiency in healthcare environments. [PERSON] et al. [5] reported that ANN-PSO models significantly outperformed conventional queue prediction techniques in terms of both accuracy and computational efficiency. This has enabled healthcare administrators to better anticipate patient flow and optimize resource distribution [16]. In emergency care settings, where patient arrival rates and service times are highly unpredictable, ANN-PSO has been particularly effective. [PERSON] [4] implemented ANN-PSO models in emergency room queue management and observed a 24% reduction in queue prediction errors compared to traditional machine learning approaches. The ability of ANN-PSO to rapidly adapt to dynamic queue conditions makes it an essential tool for improving emergency response times and minimizing patient wait times [16]. The application of ANN-PSO is not limited to emergency departments but extends to telemedicine platforms, where it has proven instrumental in optimizing virtual healthcare services. [PERSON] [6] proposed an ANN-PSO-based multi-server queue management system for telemedicine which demonstrated improved system utilization and reduced patient backlog. The study showed that ANN-PSO's adaptive learning capability enables continuous adjustments in queue predictions based on real-time patient arrival patterns. This helps to ensure optimal resource allocation and enhanced service efficiency [16]. One of the primary reasons for the superior performance of ANN-PSO in healthcare settings is its ability to mitigate the limitations of traditional queuing models.