Researchers proposed a multimodal visual retrieval-augmented generation (RAG) system for ophthalmology clinical question answering and decision support. The system, Oph-Guid-RAG, integrates query decomposition, rewriting, retrieval, reranking, and multimodal reasoning to provide traceable outputs with guideline page references. Evaluations on HealthBench show improved performance compared to GPT-5.2 and GPT-5.4, indicating the effectiveness of combining vision-based retrieval with controllable reasoning in clinical AI applications.