AI Based Healthcare Chatbot

Authors: Minal Barhate, Pranjal D. Nagmule, Sanskar M. Nalegaonkar, Pranav V. Nagur, Parth D. Nakti, Naaz K. Tadavi

Abstract: The convergence of artificial intelligence (AI) and healthcare presents enormous potential in transforming medical services, diagnostics, and information dissemination. However, a significant challenge remains in ensuring the trustworthiness and credibility of AI-generated information. Many existing AI-powered chatbots rely on large language models (LLMs) trained on diverse and non-specialized data sources, which can result in hallucinations responses that are syntactically correct but factually incorrect. This poses substantial risks in medical contexts, where accuracy is critical. To address this issue, we introduce "AD Bot," a source-grounded medical chatbot designed to generate verifiable responses strictly based on trusted medical literature. By integrating retrieval-augmented generation (RAG) with a vector-embedded knowledge base derived from medical PDFs and textbooks, AD Bot ensures reliable and evidence-based outputs. The system's architecture encompasses PDF text extraction, chunking and embedding, similarity search via vector databases, and interaction with a Hugging Face-hosted LLM. Built using Python, LangChain, Streamlit, and vector search tools such as Pinecone and Chroma, AD Bot offers a secure, modular, and user-friendly platform. This paper details the design, development, and testing of the chatbot while highlighting its implications for the future of AI-assisted healthcare.

DOI: http://doi.org/10.5281/zenodo.20728525