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ARXIV:2603.21925 · MEDICAL AI · SUBMITTED 31 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.21925MEDICAL AISUBMITTED 31 MAR · 20:30 UTCFRESHNESS STALEShuying Chen · Sen Cui · Zhong Cao · arXiv
A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases.
Opportunity summary
Pain A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases. We treat each guideline page as an independent evidence unit…
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,…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support.
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases.
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Paper Pack
10.48550/arXiv.2603.21925A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases.
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 reasoning. Ablation studies further show that reranking, routing, and retrieval design are critical for stable performance, especially under difficult settings. Overall, we show how combining visionbased retrieval with controllable reasoning can improve evidence grounding and robustness in clinical AI applications,while pointing out that further work is needed to be more complete.
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Proof status
unverified0 refs; 0 sources; 33% coverage.
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Dimensions overall score 7.0
PROBLEM
A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases. We treat each guideline page as an independent evidence unit and directly retrieve page images, pr...
METHOD
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 inf...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support.
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases. We treat each guideline page as an independent evidence unit and directly retrieve page images, preserving tables, flowcharts, and layout information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In this work, we propose Oph-Guid-RAG, a multimodal visual RAG system for ophthalmology clinical question answering and decision support.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A multimodal RAG system for ophthalmology that retrieves and reasons over guideline images to improve clinical decision support, outperforming existing models on challenging cases.
Segment
Medical AI
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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ARTIFACTS
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