Exploring Opportunities and Addressing Challenges in Designing Knowledge Graph-Enhanced RAG-Based Chatbots for Managing Radiation Toxicity in Cancer Care

Abstract

Chatbots offer essential support for patients undergoing radiation therapy by providing timely advice, tracking symptoms, and suggesting coping strategies for side effects. They act as a bridge between patients and their care teams, ensuring continuous guidance during treatment. This requires accurate, context-aware, and explainable responses, which is challenging when designing chatbots. New features and capabilities have recently been introduced to chatbot functionality via Large Language Models (LLMs) using technologies such as Retrieval-Augmented Generation (RAG), chatbots can retrieve real-time, relevant information, generating contextually relevant interactions. Traditional LLMs and RAG models still suffer from limitations such as generating hallucinated information and difficulty reasoning over complex relationships. These challenges can be addressed with knowledge graphs, which offer structured, interconnected data representations for chatbots to deliver grounded, understandable responses. Designing a chatbot that integrates knowledge graphs with RAG presents significant opportunities and challenges for developing intelligent, patient-centric conversational systems. Knowledge graphs enhance structured reasoning and accuracy, while RAG supports dynamic and personalized interactions. The paper examines architectural considerations, technological possibilities, and practical applications of such systems, especially in healthcare domains like oncology, where precision and reliability are crucial.

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