This paper discusses the challenges of using Large Language Models (LLMs) in medical chatbots for chronic disease self-management. Accordingly, we define an architecture specifically devised to deal with issues related to reliability, clinical trials, and privacy. Two solutions are compared to prevent data disclosure: a filtering mechanism for sensitive data with an external LLM, and a locally deployed LLM using open-source models. Experimental results underscore the challenges in effectively instructing the local LLM so as to provide performances comparable to GPT-3.5.
Montagna, S., Aguzzi, G., Ferretti, S., Pengo, M., Klopfenstein, L., Ungolo, M., et al. (2024). LLM-based Solutions for Healthcare Chatbots: a Comparative Analysis. In 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) (pp.346-351). Institute of Electrical and Electronics Engineers Inc. [10.1109/PerComWorkshops59983.2024.10503257].
LLM-based Solutions for Healthcare Chatbots: a Comparative Analysis
Pengo M. F.;
2024
Abstract
This paper discusses the challenges of using Large Language Models (LLMs) in medical chatbots for chronic disease self-management. Accordingly, we define an architecture specifically devised to deal with issues related to reliability, clinical trials, and privacy. Two solutions are compared to prevent data disclosure: a filtering mechanism for sensitive data with an external LLM, and a locally deployed LLM using open-source models. Experimental results underscore the challenges in effectively instructing the local LLM so as to provide performances comparable to GPT-3.5.
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