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.

翻译：有效的糖尿病管理对于维持患者健康至关重要。大语言模型（LLMs）为糖尿病管理开辟了新途径，提升了其效能。然而，当前基于LLM的方法受限于对通用信息的依赖，且缺乏与领域特定知识的整合，导致响应不准确。本文提出了一种融入知识的大语言模型驱动的对话式健康助手（CHA），专为糖尿病患者设计。我们定制并利用了开源openCHA框架，通过外部知识和分析能力增强了我们的CHA。这一整合涉及两个关键组成部分：1）纳入美国糖尿病协会饮食指南及Nutritionix信息；2）部署分析工具，以实现营养摄入量计算并与指南进行对比。我们将所提出的CHA与GPT4进行比较。评估涉及100个与日常饮食选择相关的糖尿病问题，并评估建议饮食方案可能带来的潜在风险。研究结果表明，所提出的助手在生成管理必需营养素的响应方面表现出更优性能。