Summary
Finding information quickly from lots of medical documents is tricky because of how much content there is and how it's organized. The authors looked at different ways to search through medical texts and tested how well each worked for answering medical questions. They found that retrieving just the summaries is best when the answers are concise, while using the full documents helps when more detailed information is needed. They also showed that organizing the full texts hierarchically saves storage space and speeds up searching without losing accuracy. Their work helps show how the size and format of medical collections affect the best search approach.
What this means in practice
- •For biomedical data engineers: Design scalable biomedical search systems that balance speed, storage, and answer quality using hierarchical document representations and retrieval strategies.
- •For healthcare ai developers: Build question answering tools for medical professionals that adapt retrieval methods based on the type of evidence needed for accurate responses.
Authors
Adrián Sánchez-Freire, Gonzalo Pérez-Gómez, Aitziber Atutxa, Ander Barrena
Abstract
We present a controlled, large-scale evaluation of how corpus scale, document representation, and reranking affect the effectiveness and efficiency of biomedical Retrieval-Augmented Generation (RAG) systems. Retrieval collections were progressively expanded while retaining the relevant source documents, enabling consistent comparisons across corpus sizes. We compared abstract, flat full-text, and hierarchical full-text representations across first-stage retrieval, reranking, and end-to-end multiple-choice question answering. Configurations selected on development data were evaluated on five external biomedical benchmarks and a held-out test set, while retrieval latency and index storage were measured to quantify operational cost. Performance patterns observed with small or controlled collections did not consistently persist as collections grew, and the usefulness of reranking depended on the corpus and retrieval setting. RAG improved three of four model configurations across the external benchmarks, with mean gains of up to 9.6 percentage points, and improved all four configurations on the held-out test set by 10.2-30.3 points. Abstract retrieval performed best when reference evidence was abstract-aligned, whereas full-text representations were generally more effective when richer evidence was needed. Hierarchical representation matched or outperformed flat full-text retrieval in most end-to-end comparisons while requiring 67% fewer indexed units and 44-56% less storage. These findings show that corpus scale should be treated as an explicit evaluation variable in biomedical RAG, and that the most appropriate design depends on evidence format, model behavior, and operational constraints.