D-Care: A Multi-Tone LLM-Based Chatbot Assistant for Diabetes Patients

Awais Khan Nawabi, Janos Tolgyesi, Elena Bianchi, Chiara Toffanin, Piercarlo Dondi

2025

Abstract

Diabetes is a common chronic illness projected to increase significantly in the coming years. Managing diabetes is complex, requiring patients to frequently adjust their treatments and lifestyles to prevent complications. Awareness and adherence to healthy habits are thus essential. Artificial Intelligence (AI) can assist in this effort. Recent advancements in Large Language Models (LLMs) have enabled the creation of effective chatbots to support patients. However, despite their growing use, there are still a few formal user studies on LLMs for diabetes patients. This study aims to investigate the ability of an LLM-based chatbot to provide useful and understandable information to potential patients. Specifically, the goal was to examine how variations in language and wording affect the comprehension and perceived usability of the chatbot. To this end, D-Care, a chatbot assistant based on OpenAI’s ChatGPT-4o, was developed. D-Care can generate answers in four different tones of voice, ranging from elementary to technical language. A user study with 40 participants showed that changes in tone can indeed impact the system’s comprehension and usability.

Paper Citation

in Harvard Style

Nawabi A., Tolgyesi J., Bianchi E., Toffanin C. and Dondi P. (2025). D-Care: A Multi-Tone LLM-Based Chatbot Assistant for Diabetes Patients. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 766-773. DOI: 10.5220/0013266600003911

in Bibtex Style

@conference{healthinf25,
author={Awais Nawabi and Janos Tolgyesi and Elena Bianchi and Chiara Toffanin and Piercarlo Dondi},
title={D-Care: A Multi-Tone LLM-Based Chatbot Assistant for Diabetes Patients},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={766-773},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013266600003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - D-Care: A Multi-Tone LLM-Based Chatbot Assistant for Diabetes Patients
SN - 978-989-758-731-3
AU - Nawabi A.
AU - Tolgyesi J.
AU - Bianchi E.
AU - Toffanin C.
AU - Dondi P.
PY - 2025
SP - 766
EP - 773
DO - 10.5220/0013266600003911
PB - SciTePress