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Large Language Model Integrated Healthcare Cyber-Physical Systems Architecture

Malithi Wanniarachchi Kankanamge, Syed Mhamudul Hasan, Abdur R. Shahid, Ning Yang

TL;DR

The paper addresses pressing HCPS challenges such as data fragmentation, manual data-entry burdens, and limited real-time processing by proposing a Large Language Model (LLM) integration across a three-layer HCPS architecture: data collection, data management, and application service. It advocates an LLM-integrated adapter to unify data preparation and enable natural-language interfaces, semantic-vector data management with DFS and DPC modules, and AI-enhanced visualization, reminders, and scheduling in the application layer. Key contributions include a clear architectural blueprint, the use of semantic representations for retrieval, and the integration of LLMs to automate EHR generation and support real-time decision-making, while acknowledging privacy, security, and ethical challenges. The approach aims to improve data quality, decision support, and patient outcomes, offering a pathway to more efficient, scalable, and intelligent HCPS, contingent on addressing regulatory, sustainability, and cost concerns.

Abstract

Cyber-physical systems have become an essential part of the modern healthcare industry. The healthcare cyber-physical systems (HCPS) combine physical and cyber components to improve the healthcare industry. While HCPS has many advantages, it also has some drawbacks, such as a lengthy data entry process, a lack of real-time processing, and limited real-time patient visualization. To overcome these issues, this paper represents an innovative approach to integrating large language model (LLM) to enhance the efficiency of the healthcare system. By incorporating LLM at various layers, HCPS can leverage advanced AI capabilities to improve patient outcomes, advance data processing, and enhance decision-making.

Large Language Model Integrated Healthcare Cyber-Physical Systems Architecture

TL;DR

The paper addresses pressing HCPS challenges such as data fragmentation, manual data-entry burdens, and limited real-time processing by proposing a Large Language Model (LLM) integration across a three-layer HCPS architecture: data collection, data management, and application service. It advocates an LLM-integrated adapter to unify data preparation and enable natural-language interfaces, semantic-vector data management with DFS and DPC modules, and AI-enhanced visualization, reminders, and scheduling in the application layer. Key contributions include a clear architectural blueprint, the use of semantic representations for retrieval, and the integration of LLMs to automate EHR generation and support real-time decision-making, while acknowledging privacy, security, and ethical challenges. The approach aims to improve data quality, decision support, and patient outcomes, offering a pathway to more efficient, scalable, and intelligent HCPS, contingent on addressing regulatory, sustainability, and cost concerns.

Abstract

Cyber-physical systems have become an essential part of the modern healthcare industry. The healthcare cyber-physical systems (HCPS) combine physical and cyber components to improve the healthcare industry. While HCPS has many advantages, it also has some drawbacks, such as a lengthy data entry process, a lack of real-time processing, and limited real-time patient visualization. To overcome these issues, this paper represents an innovative approach to integrating large language model (LLM) to enhance the efficiency of the healthcare system. By incorporating LLM at various layers, HCPS can leverage advanced AI capabilities to improve patient outcomes, advance data processing, and enhance decision-making.
Paper Structure (7 sections, 1 figure)

This paper contains 7 sections, 1 figure.

Table of Contents

  1. Introduction
  2. System Architecture
  3. Data Collection Layer
  4. Data Management Layer
  5. Application Service Layer
  6. Realization Challenges
  7. Conclusion

Figures (1)

  • Figure 1: The three-layered architecture of the HCPS, where the data collection layer, data management layer, and application service layer are connected to the Large Language Model (LLM).