GlaucoRAG: A Retrieval-Augmented Large Language Model for Expert-Level Glaucoma Assessment
Abstract
PURPOSE: Purpose: Accurate glaucoma assessment is challenging because of the complexity and chronic nature of the disease; therefore, there is a critical need for models that provide evidence-based, accurate assessment. The purpose of this study was to evaluate the capabilities of a glaucoma specialized Retrieval-Augmented Generation (RAG) framework (GlaucoRAG) that leverages a large language model (LLM) for diagnosing glaucoma and answering to glaucoma specific questions.
DESIGN: Evaluation of diagnostic capabilities and knowledge of emerging technologies in glaucoma assessment.
PARTICIPANTS: Detailed case reports from 11 patients and 250 multiple choice questions from the Basic and Clinical Science Course (BCSC) Self-Assessment were used to test the LLM based GlaucoRAG. No human participants were involved.
METHODS: We developed GlaucoRAG, a RAG framework leveraging GPT-4.5-PREVIEW integrated with the R2R platform for automated question answering in glaucoma. We created a glaucoma knowledge base comprising more than 1,800 peer-reviewed glaucoma articles, 15 guidelines and three glaucoma textbooks. The diagnostic performance was tested on case reports and multiple-choice questions. Model outputs were compared with the independent answers of three glaucoma specialists, DeepSeek-R1, and GPT-4.5-PREVIEW (without RAG). Quantitative performance was further assessed with the RAG Assessment (RAGAS) framework, reporting faithfulness, context precision, context recall, and answer…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.