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Benchmarking Retrieval-Augmented Generation for Medicine
Open accessAbstract
While large language models (LLMs) have achieved state-of-the-art performance on a wide range of medical question answering (QA) tasks, they still face challenges with hallucinations and outdated knowledge.Retrievalaugmented generation (RAG) is a promising solution and has been widely adopted.However, a RAG system can involve multiple flexible components, and there is a lack of best practices regarding the optimal RAG setting for various medical purposes.To systematically evaluate such systems, we propose the Medical Information Retrieval-Augmented Generation Evaluation (MIRAGE), a first-of-its-kind benchmark including 7,663 questions from five medical QA datasets.Using MIRAGE, we conducted large-scale experiments with over 1.8 trillion prompt tokens on 41 combinations of different corpora, retrievers, and backbone LLMs through the MEDRAG toolkit introduced in this work.Overall, MEDRAG improves the accuracy of six different LLMs by up to 18% over chain-of-thought prompting, elevating the performance of GPT-3.5 and Mixtral to GPT-4level.Our results show that the combination of various medical corpora and retrievers achieves the best performance.In addition, we discovered a log-linear scaling property and the "lostin-the-middle" effects in medical RAG.We believe our comprehensive evaluations can serve as practical guidelines for implementing RAG systems for medicine 1 .
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“Recently, LLMs have shown the ability to encode clinical knowledge7,8, retrieve relevant medical texts9, and perform accurate single-turn medical question-answering3,10–12.”
From AgentClinic: a multimodal benchmark for tool-using clinical AI agents (2026) · cites this paper as [9]
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“All analyses were performed on publicly available, de-identified benchmark datasets—MIRAGE [23], PubMedQA [24], and the medical licensing examination questions of Katz et al. [25]—used in accordance with their respective licenses and terms of use.”
From The economics of accuracy for medical reasoning with large language models (2026) · cites this paper as [23]
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“Similarly, without context on the MIRAGE datset, MedGemma 27B with extended reasoning (PCCR-1024) reached 80.1% which is higher than the 73.4% accuracy reported for GPT-4 without context and matched the performance of GPT-4 with access to extensive context [23].”
From The economics of accuracy for medical reasoning with large language models (2026) · cites this paper as [23]
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“This pattern appeared in approximately 40% of cases, underscoring retrieval challenges in medical RAG systems where the complexity of clinical terminology limits the effectiveness of standard mechanisms [57,58].”
From Enhancing Medical Question Answering with LLMs via a Hybrid Retrieval-Augmented Generation Framework (2026) · cites this paper as [57]
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“Such errors appeared in approximately 30% of cases, underscoring integration challenges in RAG systems where connecting retrieved facts to query-specific conclusions remain a persistent limitation [57].”
From Enhancing Medical Question Answering with LLMs via a Hybrid Retrieval-Augmented Generation Framework (2026) · cites this paper as [57]
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“The quality of RAG completion also depends on the retrieval results, such as the density or positions of query-relevant information14,22,24–26.”
From Leveraging long context in retrieval augmented language models for medical question answering (2025) · cites this paper as [26]
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Metadata
- Authors
- Xiong, G., Jin, Q., Lu, Z., & Zhang, A.
- Affiliations
- National Institutes of Health (US)
- Venue
- Findings of the Association for Computational Linguistics ACL 2024
- Published
- 2024
- Pages
- 6233-6251
- OpenAlex
- W4402670290
- Cited by
- 310
- References
- 0
- Citations by year
- 2026: 142 · 2025: 144 · 2024: 24
- FWCI
- 91.48
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2333740)
- National Institutes of Health (1R01LM014012)
- U.S. National Library of Medicine
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- retrieval-augmented generation, medical question answering, benchmark, hallucinations, large language models, medical corpora, Retriever, chain-of-thought prompting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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