Authors : Rajitha Maduri; Venkata Siva Gatta; Naveen Kumar; Bharani Kumar Deparu; Sreeja Deparu; Bhargavi Depuru; Mukesh Marwade; Gayathri K

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/5ccfr7cn

Scribd : https://tinyurl.com/32svfden

DOI : https://doi.org/10.38124/ijisrt/26apr670

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Infertility impacts a significant number of couples globally, and patients undergoing In-Vitro Fertilization (IVF) frequently require continuous access to accurate and reliable medical guidance. However, availability of healthcare professionals is often limited beyond clinical hours. This study presents an AI-driven IVF Patient Support Chatbot designed to assist patients by delivering timely and context-aware responses throughout their treatment journey. The proposed system combines retrieval-based information access with advanced language generation capabilities to improve response reliability. It utilizes Groq’s LLaMA 3.3 70B model for generating human-like responses, while semantic understanding is achieved through Sentence Transformers (all-MiniLM-L6-v2). FAISS is used as a vector database to efficiently store and retrieve IVF-related knowledge. This integrated approach ensures that responses are both relevant and grounded in domain-specific information.

Keywords : IVF Chatbot, Retrieval-Augmented Generation (RAG), LLaMA 3.3 70B, Sentence Transformers, FAISS, Healthcare AI, Patient Support Systems, NLP in Healthcare.

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