Version 1
: Received: 23 September 2024 / Approved: 24 September 2024 / Online: 25 September 2024 (04:19:36 CEST)
How to cite:
Kim, K.; Windle, J.; Christian, M.; Windle, T.; Ryherd, E.; Huang, P.-C.; Robinson, A.; Chapman, R. Framework for Integrating Large Language Models with a Ro-botic Health Attendant for Adaptive Task Execution in Patient Care. Preprints2024, 2024091947. https://doi.org/10.20944/preprints202409.1947.v1
Kim, K.; Windle, J.; Christian, M.; Windle, T.; Ryherd, E.; Huang, P.-C.; Robinson, A.; Chapman, R. Framework for Integrating Large Language Models with a Ro-botic Health Attendant for Adaptive Task Execution in Patient Care. Preprints 2024, 2024091947. https://doi.org/10.20944/preprints202409.1947.v1
Kim, K.; Windle, J.; Christian, M.; Windle, T.; Ryherd, E.; Huang, P.-C.; Robinson, A.; Chapman, R. Framework for Integrating Large Language Models with a Ro-botic Health Attendant for Adaptive Task Execution in Patient Care. Preprints2024, 2024091947. https://doi.org/10.20944/preprints202409.1947.v1
APA Style
Kim, K., Windle, J., Christian, M., Windle, T., Ryherd, E., Huang, P. C., Robinson, A., & Chapman, R. (2024). Framework for Integrating Large Language Models with a Ro-botic Health Attendant for Adaptive Task Execution in Patient Care. Preprints. https://doi.org/10.20944/preprints202409.1947.v1
Chicago/Turabian Style
Kim, K., Anthony Robinson and Reid Chapman. 2024 "Framework for Integrating Large Language Models with a Ro-botic Health Attendant for Adaptive Task Execution in Patient Care" Preprints. https://doi.org/10.20944/preprints202409.1947.v1
Abstract
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.
Keywords
medical service robot; Large Language Model (LLM); Robotic Health Attendant; healthcare robot; ChatGPT
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
