The development of intelligent medical service robots for patient care presents significant challenges, particularly in integrating diverse knowledge sources and enabling robots to autonomously perform tasks in dynamic and unpredictable healthcare environments. This study introduces a novel framework that combines large language models with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant. Utilizing OpenAI’s ChatGPT, the system processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the robot successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the language model and predefined task specifications. The results demonstrate the potential of language models to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural language.

The development of intelligent medical service robots for patient care presents significant chal-lenges, particularly in integrating diverse knowledge sources and enabling robots to autono-mously perform tasks in dynamic and unpredictable healthcare environments. This study in-troduces a novel framework that combines large language models (LLMs) with healthcare-specific knowledge and robotic operations to enhance autonomous task execution for a Robotic Health Attendant (RHA). Utilizing OpenAI’s ChatGPT, the RHA processes structured information about patient care protocols and unstructured human inputs to generate context-aware robot actions. A prototype system was tested in a simulated patient room where the RHA successfully performed both simple individual actions and complex tasks involving the execution of multiple actions, based on real-time dialogues with the LLM and predefined task specifications. The results demonstrate the potential of LLMs to reduce the reliance on hardcoded logic and provide healthcare professionals with the ability to interact with robotic systems through natural lan-guage.

Despite recent advances in healthcare robotics, most existing systems remain limited to single-purpose functions and lack the flexibility to collaborate dynamically with clinicians and facility systems. To address these limitations, this study presents an LLM-orchestrated framework for a multifunctional Robotic Health Attendant (RHA) that enables robot actions and environment interactions to be coordinated in healthcare environments. Within this framework, the RHA functions as a multifunctional nursing assistant capable of performing physical, communicative, and informational tasks through natural-language interaction. Tasks are expressed in natural language and decomposed into coordinated behaviors across three functional branches: physical, for navigation, object manipulation, and delivering medication; communicational, for dialog with patients and clinicians; and informational, for retrieving and summarizing clinical knowledge, such as patient education on complex heart transplant procedures. The framework integrates multiple Large Language Models (LLMs) and sensing nodes to combine facility data, patient information, and clinician commands, enabling robots and building systems to act in a context-aware manner through coordinated task execution across robotic and environmental components. Implemented in a simulated environment, the framework demonstrates the feasibility of executing representative tasks through LLM-based orchestration, serving as a proof-of-concept toward integrated robotic assistance in healthcare settings.