From Large Language Models to Large Action Models: Redefining Communication Intelligence in Healthcare Internet of Things Applications

The continued growth of Healthcare Internet of Things (HC-IoT) applications has transformed patient monitoring, diagnosis, and treatment from traditional practices into intelligent, data-driven processes that support a wide range of healthcare tasks. However, the effectiveness of these systems depends on reliable, secure, and context-aware communication among heterogeneous medical devices, communication networks, and clinical infrastructures. Large Language Models (LLMs) have recently been explored to improve data interpretation, coordination, and decision support in HC-IoT applications. However, most existing solutions use LLMs for perception and reasoning, while device control, routing, bandwidth allocation, and resource management still depend on predefined rules. Therefore, they cannot adapt the communication process in real time when network conditions, device status, or patient needs change. Large Action Models (LAMs) offer a possible path beyond this limitation by connecting reasoning with autonomous action. Through tools and control interfaces, they can reconfigure network routes, allocate bandwidth, coordinate devices, and manage communication resources according to changing system goals. In this survey, we examine how LAMs can support communication intelligence in HC-IoT by combining perception, reasoning, and real-time control. For this, we cover the HC-IoT application communication architectures, protocols, and standards published from 2017 to 2026, with particular attention to 5G/6G networks, edge and cloud collaboration, adaptive routing, and quality-of-service management. We also present a unified taxonomy and architectural framework for HC-IoT applications using LAMs to manage latency, reliability, throughput, and resource allocation across medical networks. Finally, we outline open challenges and future directions for tactile and ultra-reliable healthcare communications to guide the development of efficient and intelligent LAM-enabled frameworks for next-generation HC-IoT applications.