[2603.21925] Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support

Computer Science > Artificial Intelligence arXiv:2603.21925 (cs) [Submitted on 23 Mar 2026] Title:Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support Authors:Shuying Chen, Sen Cui, Zhong Cao View a PDF of the paper titled Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support, by Shuying Chen and 2 other authors View PDF 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 provides traceable outputs with guideline page references. We evaluate our method on HealthBench using a doctor-based scoring protocol. On the hard subset, our approach improves the overall score from 0.2969 to 0.3861 (+0.0892, +30.0%) compared to GPT-5.2, and achieves higher accuracy, improving from 0.5956 to 0.6576 (+0.0620, +10.4%). Compared to GPT-5.4, our method achieves a larger accuracy gain of +0.1289 (+24.4%). These results show that our method is more effective on challenging cases that require precise, evidence-based reasonin...