Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support
Quick summary
arXiv:2603.21925v2 Announce Type: replace Abstract: In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support. We treat each guideline page as an independent evidence unit and directly retrieve page images, preserving tables, flowcharts, and layout information. We further design a controllable retrieval framework with routing and filtering, which selectively introduces external evidence and reduces noise. The system integrates query decomposition, query rewriting, retrieval, reranking, and multimodal reasoning, and
Key takeaways
- arXiv:2603.21925v2 Announce Type: replace Abstract: In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support.
- We treat each guideline page as an independent evidence unit and directly retrieve page images, preserving tables, flowcharts, and layout information.
- We further design a controllable retrieval framework with routing and filtering, which selectively introduces external evidence and reduces noise.
Why it matters
“Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
Member comments