RetiBridge: Bridging Quantitative Retinal Biomarkers and Qualitative Diagnosis with a Knowledge-Guided Multimodal Large Language Model

Retinal biomarkers captured by color fundus photography and optical coherence tomography provide clinically valuable evidence for both ocular and systemic diseases. Multimodal large language models (MLLMs) have shown promise for retinal image interpretation, yet existing ophthalmic models rarely quantify these clinically relevant biomarkers or explicitly translate their measurements into qualitative, evidence-grounded diagnostic conclusions. To address this gap, we introduce RetiBridge, a knowledge-guided multimodal large language model that jointly analyzes color fundus photography (CFP), optical coherence tomography (OCT), and text, explicitly bridging quantitative retinal biomarkers to qualitative clinical sub-inferences and coherent diagnostic conclusions. RetiBridge combines knowledge-guided instruction generation, OCT-biomarker alignment, and supervised multimodal instruction tuning to learn a biomarker-grounded quantitative-to-qualitative diagnostic pathway. Using 15,611 paired CFP-OCT samples from UK Biobank with 31 OCT and 6 CFP biomarkers, we construct the Grounded Ophthalmic Understanding benchmark to evaluate diagnostic classification, report generation quality, and fine-grained clinical quality. Despite using only LoRA-based fine-tuning of a 7B-parameter Qwen2 backbone, RetiBridge outperforms all evaluated open-source 7B and 32B baselines, achieving the highest quantitative accuracy, evidence grounding, coverage completeness, and BERTScore, while surpassing OpenAI o3 on these key biomarker-grounded metrics. Our code and data are released in the RetiBridge repository.