Medical question answering systems must provide responses that are not only understandable, but also grounded in reliable sources. This paper presents the MedQuAD Healthcare Assistant, a retrieval-augmented chatbot for patient-facing educational medical question answering. The system uses the MedQuAD dataset as a medical knowledge source, retrieves relevant question-answer records using dense vector search, and generates cited responses with Qwen2.5-7B-Instruct. The evaluation compares a No-RAG baseline with MiniLM and BGE-small retrieval configurations across multiple top-k settings, using retrieval metrics, RAGAS answer-quality metrics, latency, and human evaluation. BGE-small with k = 10 achieved the strongest overall balance, reaching 1.000 Exact QA Hit@10 and 0.911 Exact QA MRR, while also providing the highest context recall among RAG configurations. These results show that dense-retrieval RAG can improve reliable information access, while still requiring expert validation before clinical use.