EyeRAG system reduces factual inaccuracies in ophthalmic clinical dialogue.
EyeRAG: graph retrieval-augmented generation for safe and accurate clinical dialogue in ophthalmology.
Kaikai Zhao et al. · NPJ digital medicine · 2026
Background
Large language models promise to transform ophthalmic clinical communication but face challenges from factual inaccuracies (hallucinations) and limited domain knowledge.
Purpose
This study introduces EyeRAG, a guideline-grounded GraphRAG system for ophthalmic dialogue that integrates OphthaKG, a domain-specific knowledge graph, constructed exclusively from clinical guidelines.
3.3%
Results
the percentage of factual inaccuracies in dialogue was reduced to this low level
n = 120 clinical scenarios
More results
Validated by LLM-as-a-judge and board-certified ophthalmologists on internal/external datasets, EyeRAG outperformed Vanilla LLMs and standard RAG.
It serves as a tool for patient interpretation rather than clinical decision-making, assisting in patient counselling and tele-ophthalmology by translating complex findings into accessible dialog under professional oversight.