An Intelligent Healthcare Conversational Assistant Using Gamma LLM V2 with Comparative Evalu-ation of Transformer-Based BERT Models
Authors-T.Thirumalash, P.Mahalaxmi
Keyword-Healthcare Chatbot, Gamma LLM V2, Medical Question Answering, Large Language Models (LLMs), BERT, MedBERT, RoBERTa, Sentence-BERT (SBERT), LangChain, Pinecone Vec-tor Database, Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), Semantic Search, Artificial Intelligence, Medical Decision Support.
The rapid advancement of artificial intelligence has significantly improved the capabilities of intelligent healthcare applications by enabling automated medical consultation and decision sup-port. This research presents a medical conversational assistant powered by Gamma LLM V2, designed to provide accurate, context-aware responses related to symptoms, medications, diseas-es, and dietary recommendations. The proposed framework employs Retrieval-Augmented Generation (RAG) by combining semantic vector retrieval with a large language model to im-prove the reliability of generated responses. Medical knowledge is extracted from a comprehen-sive medical reference document, segmented into meaningful text chunks, transformed into vector embeddings, and stored within the Pinecone vector database for efficient semantic retriev-al. LangChain is utilized to orchestrate document retrieval and prompt generation, while Flask and Streamlit provide backend services and an interactive user interface. To evaluate the effec-tiveness of the proposed chatbot, Gamma LLM V2 is compared with widely adopted transform-er-based language models including BERT, RoBERTa, MedBERT, and Sentence-BERT (SBERT). Experimental analysis demonstrates that Gamma LLM V2 consistently produces more accurate, contextually relevant, and coherent responses while maintaining superior perfor-mance across medical question-answering tasks. The comparative evaluation shows performance scores of 0.95 for MedBERT, 0.92 for SBERT, 0.86 for BERT, and 0.77 for RoBERTa, con-firming the effectiveness of the proposed approach. The developed system offers a scalable and practical AI-assisted healthcare solution that improves access to preliminary medical guidance while reducing response time and enhancing user experience.
Doi-[https://doi.org/10.5281/zenodo.21735650]