mlsanityData to 14 Oct 2026

Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation

arXiv 2505.07917 ↗First posted 12 May 2025cs.IR cs.AI cs.DBcited by 2

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 9 of its 15 references that are in the map. First posted on arXiv 12 May 2025.

Builds on

9 of 15 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024MedExpQA: Multilingual Benchmarking of Large Language Models for Medical Question Answering8 Apr
  2. 2024Benchmarking Retrieval-Augmented Generation for Medicine20 Feb

Other references · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  4. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
  5. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  6. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 185
  7. 2024Reliable, Adaptable, and Attributable Language Models with Retrievalcited by 73

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Retrieval Augmented Biomedical Question Answering with Weak Question Recovery and Neural Reranking for BioASQ Task 14bcited by 1
  2. 2026CORTEX: Learning to Share and Specialize in Dense Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
  2. 2026When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG
  3. 2026Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study
  4. 2024Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
  5. 2026A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
  6. 2025Beyond Retrieval: Ensembling Cross-Encoders and GPT Rerankers with LLMs for Biomedical QA
  7. 2025Biomedical Literature Q&A System Using Retrieval-Augmented Generation (RAG)
  8. 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering