Medibot: UTAR hospital AI health companion - Tong, Jia Seng (2025)
This proposal introduces a project aimed at enhancing the user experience on UTAR Hospital’s platform by developing an English-Chinese multilingual chatbot that provides personalized medical guidance through doctor recommendations and disease prediction. The chatbot leverages advanced technologies such as the LLaMA transformer model, Retrieval-Augmented Generation (RAG), and natural language processing (NLP) to interact with users in a natural, friendly, and informative way. The core of the project lies in the chatbot’s ability to understand user-described symptoms and predict the most likely disease category using machine learning techniques, such as Random Forest Classifier, Logistic Regression, Xgboost Classifier. Based on the prediction, the chatbot recommends suitable doctors from UTAR Hospital’s Traditional and Complementary Medicine Centre for further consultation. RAG plays a key role in generating human-like responses by combining retrieved information with natural language generation, ensuring the conversation feels more engaging and helpful. The chatbot’s multilingual capability, supporting both English and Chinese, enables it to assist a wider and more diverse range of users, particularly in Malaysia’s multicultural context. Additionally, the system incorporates a similarity search mechanism using a temporary vector database to improve the accuracy and relevance of responses. It also features an integrated online appointment system to streamline consultation scheduling and reduce reliance on manual processes. Overall, this project aims to enhance healthcare accessibility through a multilingual chatbot that supports a diverse user base by providing symptom-based disease prediction, personalized doctor recommendations, and streamlined appointment scheduling based on the predicted disease category.