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<p>With the increasing prevalence of chronic diseases, global public health systems are facing unprecedented challenges, prompting active exploration of innovative health management solutions powered by artificial intelligence (AI). In recent years, strategies such as intervention personalization and data-driven decision making have played a critical role in improving the effectiveness of chronic disease prevention and control. Simultaneously, AI assisted conversational systems have been increasingly applied to patient education and health support services, demonstrating greater immediacy and interactivity compared to traditional care models. Moreover, the deep integration of human expertise with AI systems is driving an irreversible transformation toward smarter and more efficient healthcare infrastructures. In response to these developments, this study presents a generative AI-based health advisory system specifically designed to deliver real-time, personalized recommendations for individuals with chronic diseases, obesity, or metabolic syndrome. The system utilizes LINE, a widely adopted communication platform in Taiwan, as the primary user interface, and integrates the ChatGPT API with Retrieval-Augmented Generation (RAG) techniques. By leveraging users’ basic physiological profiles and accumulated health data, the system dynamically generates dietary suggestions, drug interaction alerts, and interpretations of medical checkup results.</p>

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Generative-AI Based Health Advisory System for Patients with Chronic Diseases

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Ya-Wen Chuang,
  • Ren-Chuan Chang,
  • Yu-Tsung Cheng,
  • Shih-Ting Huang,
  • Tung-Min Yu,
  • Kuo-Hsiung Shu

摘要

With the increasing prevalence of chronic diseases, global public health systems are facing unprecedented challenges, prompting active exploration of innovative health management solutions powered by artificial intelligence (AI). In recent years, strategies such as intervention personalization and data-driven decision making have played a critical role in improving the effectiveness of chronic disease prevention and control. Simultaneously, AI assisted conversational systems have been increasingly applied to patient education and health support services, demonstrating greater immediacy and interactivity compared to traditional care models. Moreover, the deep integration of human expertise with AI systems is driving an irreversible transformation toward smarter and more efficient healthcare infrastructures. In response to these developments, this study presents a generative AI-based health advisory system specifically designed to deliver real-time, personalized recommendations for individuals with chronic diseases, obesity, or metabolic syndrome. The system utilizes LINE, a widely adopted communication platform in Taiwan, as the primary user interface, and integrates the ChatGPT API with Retrieval-Augmented Generation (RAG) techniques. By leveraging users’ basic physiological profiles and accumulated health data, the system dynamically generates dietary suggestions, drug interaction alerts, and interpretations of medical checkup results.