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

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    This study introduces a specialized conversational health agent (CHA) for diabetes management. The knowledge-infused CHA provides more accurate nutritional advice compared to general models like GPT4.

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    Area of Science:

    • Artificial Intelligence in Healthcare
    • Diabetes Management Technologies
    • Computational Health

    Background:

    • Effective diabetes management is vital for patient health.
    • Large Language Models (LLMs) offer potential for diabetes care.
    • Current LLM approaches lack domain-specific knowledge, leading to inaccuracies.

    Purpose of the Study:

    • To develop a knowledge-infused LLM-powered conversational health agent (CHA) for diabetic patients.
    • To enhance an open-source CHA framework with external knowledge and analytical tools.
    • To improve the accuracy and relevance of LLM-generated responses for diabetes management.

    Main Methods:

    • Customized and leveraged the open-source openCHA framework.
    • Integrated American Diabetes Association dietary guidelines and Nutritionix data.
    • Deployed analytical tools for nutritional intake calculation and comparison.
    • Evaluated the CHA using 100 diabetes-related questions on meal choices and dietary risks.

    Main Results:

    • The proposed CHA demonstrated superior performance in generating responses for managing essential nutrients.
    • The knowledge-infused agent provided more accurate and contextually relevant dietary advice.
    • Comparison with GPT4 indicated significant improvements in specialized diabetes care.

    Conclusions:

    • Knowledge infusion and analytical capabilities significantly enhance LLM-based conversational health agents for diabetes management.
    • The developed CHA offers a promising tool for personalized and accurate dietary guidance for diabetic patients.
    • This approach addresses the limitations of general LLMs in specialized health domains.