AI evidence extraction

The Role of Retrieval-Augmented Generation in Improving Factual Accuracy for Medical Large Language Models

Eason Ni · Scholarly review . · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.70121/001c.158711

Methodology & findings

Study design

Narrative literature review synthesizing existing frameworks, case studies, and research findings on RAG systems and their application to medical large language models.

Main result

The paper finds that "RAG solves these issues by creating a pipeline with a retriever that gets relevant information from a corpus and combining the retrieved information with the question before having the model compose an answer" (Singh et al., 2025). Key advances include evolution from Naïve RAG to Advanced, Modular, and Agentic RAG paradigms. Case studies demonstrate significant improvements: "MedRAG outperformed state-of-the-art RAG models on the Chronic Pain Diagnostic Dataset (CPDD) and the DDXPlus dataset, exceeding next-best scores by 11.32% and 1.23% respectively" (Zhao et al., 2025), and "BiomedRAG improved the original MedLLaMA 13B and LLaMA2 13B LLMs by 34.67% and 22.43%, respectively, in F1 score for triple extraction on the GIT dataset" (Li et al., 2025).

Research paradigm

Interpretive/Qualitative - literature-based synthesis without systematic protocol

Author conclusions

The authors conclude that "The introduction of the RAG pipeline has significantly enhanced LLM accuracy in domain-specific areas like healthcare and biomedicine" and that "the evolution from Naïve and Advanced RAG pipelines to Modular and, more recently, Agentic RAG, reflects a shift toward increasingly adaptive and autonomous systems capable of addressing the unique demands of medical question-answering." However, they emphasize that "the deployment of RAG in medical settings is constrained by technical and ethical challenges" and that "benchmarking efforts, such as MIRAGE, represent important progress toward the standardized evaluation of individual components of RAG. However, these current benchmarks fall behind the rapidly evolving architectures of modern RAG systems, particularly those with autonomous or multi-agent behaviors."

Risk of bias

Corpus curation bias and quality dependency; Algorithmic biases in retrievers due to uneven distribution of demographic attributes in knowledge sources; Training data bias in biomedical literature (English dominance, limited non-Western medical practices); Selection bias in available knowledge sources (multilingual support gaps); Selection bias in literature review (no systematic search methodology described); Publication bias favoring positive RAG implementation studies; Algorithmic bias in retrievers from uneven demographic attributes in knowledge sources; Bias from limited training data and existing disparities in medical knowledge bases; Language bias: English dominates public biomedical repositories, affecting performance in low-resource languages

Limitations

The paper identifies several limitations in current RAG systems
"RAG systems are also difficult to benchmark, given the complexity and tunability of the parts" (Xiong et al., 2024)
Additionally, "MIRAGE currently only supports the vanilla RAG architecture, lacking support for active or agentic RAG systems." On context handling: "LLMs suffer decreased performance in accuracy for tasks when given larger contexts" with "reported performance drops ranging from 13.5% to 85% across evaluated models" (Du et al., 2025)
The authors note that "RAG tries to address the main problem of transparency and hallucinations, but it does not eliminate all fundamental LLM challenges and even introduces new ones" (Bunnell et al., 2025).

Open questions raised

The paper identifies multiple research gaps: (1) lack of research on algorithmic biases of retrievers in RAG pipelines; (2) absence of specialized benchmarks for agentic capabilities including multi-agent collaboration; (3) inadequate multilingual support due to English dominance in biomedical repositories; (4) need for deeper understanding of Retriever-Generator interaction; (5) unexplained contradictory findings (e.g., Cuconasu et al. finding that adding random documents improves accuracy by up to 35%); (6) limited exploration of multimodal RAG systems integrating imaging, time series, and audio data.
Dearth of research on algorithmic biases of retrievers in RAG pipelines
Lack of specialized benchmarks and datasets for agentic capabilities (multi-agent collaboration, dynamic adaptability)
Inadequate exploration of multilingual support (English dominance in biomedical repositories)
Limited understanding of Retriever-Generator interaction and their loose coupling
Inadequate exploration of multimodal RAG systems beyond text (imaging, time series, audio)

Data: MIRAGE benchmark (7,663 questions from 5 datasets: MMLU-Med, MedQA-US, MedMCQA, PubMedQA, BioASQ-Y/N); Chronic Pain Diagnostic Dataset (CPDD); DDXPlus dataset; GIT dataset; MMLU-Med; MedQA-US; MedMCQA; PubMedQA; BioASQ-Y/N; MIRAGE: Medical Information Retrieval-Augmented Generation Evaluation benchmark comprising 7,663 questions from 5 datasets (MMLU-Med, MedQA-US, MedMCQA, PubMedQA, BioASQ-Y/N)Code: MEDRAG toolkit developed by Xiong et al. (2024); MEDRAG toolkit (developed by Xiong et al. 2024); MEDRAG toolkit developed by Xiong et al. (2024) for systematic implementations of RAG pipelines for medical QAExtracted from: pdfAgreement 56%

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