Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support
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 provides traceable outputs with guideline page references. We evaluate our method on HealthBench using a doctor-based scoring protocol. On the hard s...
| Authors | Shuying Chen, Sen Cui, Zhong Cao |
|---|---|
| Primary category | cs.AI |
| Published | 23 March 2026 |
| Last updated at source | 17 September 2026 |
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