Published in Data Science and Engineering (DSE) Record 2025 Vol. 6 No. 1 pp. 131-167
Abstract
In recent years, Large Language Models (LLMs) have demonstrated signifi-cant potential in various applications, including healthcare, education, and customer support. This study investigates the integration of LLMs into group chat environments to facilitate medical counseling between doctors and heart disease patients. Traditional chatbot systems primarily operate in one-on-one interactions, which can lead to redundant queries and ineffi-ciencies in medical consultations. This research introduces a novel chatbot system designed for group chat settings, allowing multiple users and medi-cal professionals to interact seamlessly within the same conversation.The chatbot system retrieves medical knowledge from a predefined document database using an information retrieval model to ensure responses are rele-vant and accurate. A verification mechanism is integrated, enabling doctors to review and validate chatbot-generated responses before they are present-ed to patients. The study employs hypothesis testing and real-world evalua-tions to measure chatbot performance across three key dimensions: re-sponse accuracy, response speed, and user satisfaction. Experimental re-sults indicate that group chat environments improve communication effi-ciency, reduce repetitive queries, and enhance patient engagement compared to traditional one-on-one chatbot interactions.Furthermore, user feedback highlights the strengths and limitations of the proposed system. While the chatbot successfully provides relevant medical information, challenges re-main in ensuring response accuracy, reducing response time, and improving contextual understanding in group conversations. Future work will focus on refining chatbot algorithms, enhancing natural language processing capa-bilities, and expanding the medical knowledge base to support a wider range of healthcare scenarios. This research underscores the potential of LLMs in transforming digital healthcare support, making medical consulta-tions more efficient, accessible, and collaborative.