arXiv:2505.07917v2 Announce Type: replace-cross 
Abstract: Biomedical question-answering (QA) systems require effective retrieval and generation components to ensure accuracy, efficiency, and scalability. This study systematically examines a Retrieval-Augmented Generation (RAG) system for biomedical QA, evaluating retrieval strategies and response time trade-offs. We first assess state-of-the-art retrieval methods, including BM25, BioBERT, MedCPT, and a hybrid approach, alongside common data stores such as Elasticsearch, MongoDB, and FAISS, on a ~10% subset of PubMed (2.4M documents) to measure indexing efficiency, retrieval latency, and retriever performance in the end-to-end RAG system. Based on these insights, we deploy the final RAG system on the full 24M PubMed corpus, comparing different retrievers' impact on overall performance. Evaluations of the retrieval depth show that retrieving 50 documents with BM25 before reranking with MedCPT optimally balances accuracy (0.90), recall (0.90), and response time (1.91s). BM25 retrieval time remains stable (82ms), while MedCPT incurs the main computational cost. These results highlight previously not well-known trade-offs in retrieval depth, efficiency, and scalability for biomedical QA. With open-source code, the system is fully reproducible and extensible.

قدمت دراسة نظامًا لتوليد الأسئلة البيولوجية المعززة بالاسترجاع (RAG) ، حيث تم تقييم استراتيجيات الاسترجاع المختلفة وكفاءتها على مجموعة فرعية من بيانات PubMed. تقيم الدراسة طرقًا متطورة مثل BM25 و BioBERT ، وتقيس كفاءة الفهرسة وأداء الاسترجاع قبل نشر النظام على مجموعة بيانات PubMed الكاملة.

Un estudio ha presentado un sistema de Generación Aumentada por Recuperación (RAG) para la respuesta a preguntas biomédicas, evaluando diversas estrategias de recuperación y su eficiencia en un subconjunto de datos de PubMed. La investigación evalúa métodos de vanguardia como BM25 y BioBERT, midiendo la eficiencia de indexación y el rendimiento de recuperación antes de implementar el sistema en el corpus completo de PubMed.

Une étude a introduit un système de génération augmentée par récupération (RAG) pour le questionnement biomédical, évaluant diverses stratégies de récupération et leur efficacité sur un sous-ensemble de données de PubMed. La recherche évalue des méthodes à la pointe comme BM25 et BioBERT, mesurant l'efficacité de l'indexation et la performance de récupération avant de déployer le système sur l'ensemble du corpus de PubMed.

A study has introduced a Retrieval-Augmented Generation (RAG) system for biomedical question answering, evaluating various retrieval strategies and their efficiency on a subset of PubMed data. The research assesses state-of-the-art methods like BM25 and BioBERT, measuring indexing efficiency and retrieval performance before deploying the system on the full PubMed corpus.

Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation

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Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation

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