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This article investigates the possible synergy between Large Language Models (LLMs) and 6G edge computing in the Augmented Intelligence of Things (AIoT) architecture for remote healthcare systems. We aim to utilize the advanced natural language processing powers of LLMs, along with the high­speed and minimal delay networks provided by 6G technology, to improve communication and collaboration among patients, healthcare practitioners, and medical infrastructure. Our proposal introduces a new framework that combines LLMs with 6G edge computing settings. The goal is to enhance real-time, efficient, and intelligent interactions in remote healthcare services. This strategy is especially pertinent in the context of the 5G/6G era, when the integration of AIoT with LLMs can greatly enhance patient care, diagnosis, and the general effectiveness of healthcare delivery systems. We showcase the efficacy of LLM ­empowered AIoT in enhancing remote healthcare services by employing a thorough approach that includes simulations and real-world case studies. The article also discusses the difficulties of achieving this integration and examines potential future developments, emphasizing the revolutionary effect of merging LLMs with 6G edge computing in intelligent healthcare systems.

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Optimizing Remote Medical Services with AloT: Integration of Large Language Models and 6G Edge Computing

  • 会议论文

  • 发表日期：

    2025年

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  • Tao Zhang,
  • Xiaoyao Xu,
  • Yifan Wang,
  • Rongqun Peng,
  • Michel Kadoch

摘要

This article investigates the possible synergy between Large Language Models (LLMs) and 6G edge computing in the Augmented Intelligence of Things (AIoT) architecture for remote healthcare systems. We aim to utilize the advanced natural language processing powers of LLMs, along with the high­speed and minimal delay networks provided by 6G technology, to improve communication and collaboration among patients, healthcare practitioners, and medical infrastructure. Our proposal introduces a new framework that combines LLMs with 6G edge computing settings. The goal is to enhance real-time, efficient, and intelligent interactions in remote healthcare services. This strategy is especially pertinent in the context of the 5G/6G era, when the integration of AIoT with LLMs can greatly enhance patient care, diagnosis, and the general effectiveness of healthcare delivery systems. We showcase the efficacy of LLM ­empowered AIoT in enhancing remote healthcare services by employing a thorough approach that includes simulations and real-world case studies. The article also discusses the difficulties of achieving this integration and examines potential future developments, emphasizing the revolutionary effect of merging LLMs with 6G edge computing in intelligent healthcare systems.