A Retrieval-Augmented Generation Framework for Cancer Health Question Answering Using Semantic Filtering and GPT-4o-mini

This work presents a lightweight, domain-specific RAG framework for cancer QA that uses semantic embedding-based topic filtering to construct a focused 752-pair corpus from MedQuAD. It demonstrates substantial performance gains over BM25 (64.1% MRR improvement) and non-RAG GPT-4o-mini (10.4% BERTScore improvement), with expert evaluation confirming higher safety and accuracy. The ablation studies systematically identify optimal retrieval depth (k=4) and embedding model, while the framework is designed for scalable, non-diagnostic consumer health information access.