AI-enhanced telemedicine: transforming resource allocation and cost-efficiency analysis via advanced queueing model.

The increasing demand for telemedicine makes conventional queueing approaches inadequate for meeting dynamic and priority-driven service needs. Therefore, a more advanced queueing mechanism is necessary to address these requirements. This paper integrates an advanced queueing model with AI-scheduling, implemented using deep reinforcement learning, to optimize digital healthcare by adjusting doctor availability dynamically and prioritizing patient care based on urgency and time of arrival.