Medical Emergency Decision-Support Systems (MEDSSs) are benefiting from Artificial Intelligence (AI) techniques that are supported by Large Language Models (LLMs). In this context, based on feedback from doctors and other medical personnel, LLMs can be fine tuned to provide better diagnostics by deviating from the general purpose behavior of commercial grade LLMs. Moreover, the results obtained by the different fine-tuned LLMs can be combined and processed by multi-agent Super Learners (SLs) that focus on specific aspects of MEDSS. Because of the dynamic nature of emergency medicine where the interaction between doctors and MEDSSs is typically by means of speech, constrained environments cannot rely on traditional data-driven mechanisms of media transmission. This chapter considers a rural environment where there is no infrastructure supporting mobile data communications and modern ad-hoc Low Power Wide Area Network (LPWAN) topologies are used instead. The overall idea is to rely on low bit rate speech coder-encoder (codecs) to enable packetization over multi-hop Long Range (LoRa) networks that support the transmission of speech to edge gateways for propagation to the MEDSS. While MEDSSs rely on standard Internet suite protocols that enable REpresentional State Transfer (REST) Application Program Interface (APIs), speech as well as medical device sensor data can be propagated to the gateways by means of Internet of Things (IoT) protocols.