# Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients

arXiv: https://arxiv.org/abs/2402.10153 · HTML: https://mlsanity.com/paper/2402.10153/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 4 of 4)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://mlsanity.com/paper/2310.02374/) (2023)

## Built on by (most cited first, 9 of 9)

- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [Advancing and Benchmarking Personalized Tool Invocation for LLMs](https://mlsanity.com/paper/2505.04072/) (2025)
- [Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis](https://mlsanity.com/paper/2406.16252/) (2024)
- [Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs](https://mlsanity.com/paper/2410.05684/) (2024)
- [Accuracy and Consistency of LLMs in the Registered Dietitian Exam: The Impact of Prompt Engineering and Knowledge Retrieval](https://mlsanity.com/paper/2408.02964/) (2024)
- [SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching](https://mlsanity.com/paper/2509.04752/) (2025)
- [Personalized Causal Graph Reasoning for LLMs: An Implementation for Dietary Recommendations](https://mlsanity.com/paper/2503.00134/) (2025)
- [If Only My CGM Could Speak: A Privacy-Preserving Agent for Question Answering over Continuous Glucose Data](https://mlsanity.com/paper/2604.17133/) (2026)
- [Investigating the Ability of Large Language Models to Analyze Recipes for Diabetes](https://mlsanity.com/paper/2609.03967/) (2026)

## Abstract (arXiv)

Effective diabetes management is crucial for maintaining health in diabetic patients. Large Language Models (LLMs) have opened new avenues for diabetes management, facilitating their efficacy. However, current LLM-based approaches are limited by their dependence on general sources and lack of integration with domain-specific knowledge, leading to inaccurate responses. In this paper, we propose a knowledge-infused LLM-powered conversational health agent (CHA) for diabetic patients. We customize and leverage the open-source openCHA framework, enhancing our CHA with external knowledge and analytical capabilities. This integration involves two key components: 1) incorporating the American Diabetes Association dietary guidelines and the Nutritionix information and 2) deploying analytical tools that enable nutritional intake calculation and comparison with the guidelines. We compare the proposed CHA with GPT4. Our evaluation includes 100 diabetes-related questions on daily meal choices and assessing the potential risks associated with the suggested diet. Our findings show that the proposed agent demonstrates superior performance in generating responses to manage essential nutrients.