mlsanityData to 7 Oct 2026

Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications

arXiv 2501.02460 ↗First posted 5 Jan 2025cs.CLcited by 13

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

Abstract (arXiv)

Large language models hold promise for addressing medical challenges, such as medical diagnosis reasoning, research knowledge acquisition, clinical decision-making, and consumer health inquiry support. However, they often generate hallucinations due to limited medical knowledge. Incorporating external knowledge is therefore critical, which necessitates multi-source knowledge acquisition. We address this challenge by framing it as a source planning problem, which is to formulate context-appropriate queries tailored to the attributes of diverse sources. Existing approaches either overlook source planning or fail to achieve it effectively due to misalignment between the model's expectation of the sources and their actual content. To bridge this gap, we present MedOmniKB, a repository comprising multigenre and multi-structured medical knowledge sources. Leveraging these sources, we propose the Source Planning Optimisation method, which enhances multi-source utilisation. Our approach involves enabling an expert model to explore and evaluate potential plans while training a smaller model to learn source alignment. Experimental results demonstrate that our method substantially improves multi-source planning performance, enabling the optimised small model to achieve state-of-the-art results in leveraging diverse medical knowledge sources.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 39 of its 70 references that are in the map. First posted on arXiv 5 Jan 2025.

Builds on

39 of 70 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systems17 Aug
  2. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions1 Aug
  3. 2024Benchmarking Retrieval-Augmented Generation for Medicine20 Feb
  4. 2023Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping11 Dec

Other references · 35

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  5. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  6. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,324
  7. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,271
  8. 2023AWQ: Activation-aware Weight Quantization for LLM Compression and Accelerationcited by 1,234
  9. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  10. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 480
  11. 2024Capabilities of Gemini Models in Medicinecited by 231
  12. 2022Can large language models reason about medical questions?cited by 194
23 more
  1. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 182
  2. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
  3. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  4. 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 121
  5. 2023CMB: A Comprehensive Medical Benchmark in Chinesecited by 113
  6. 2023ExpertQA: Expert-Curated Questions and Attributed Answerscited by 96
  7. 2022DDXPlus: A New Dataset For Automatic Medical Diagnosiscited by 87
  8. 2024LUQ: Long-text Uncertainty Quantification for LLMscited by 79
  9. 2024RaFe: Ranking Feedback Improves Query Rewriting for RAGcited by 47
  10. 2024UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systemscited by 38
  11. 2024BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrieverscited by 38
  12. 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 38
  13. 2024KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniquescited by 37
  14. 2024BlendFilter: Advancing Retrieval-Augmented Large Language Models via Query Generation Blending and Knowledge Filteringcited by 29
  15. 2024Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversationcited by 23
  16. 2021Literature-Augmented Clinical Outcome Predictioncited by 20
  17. 2024ReflecTool: Towards Reflection-Aware Tool-Augmented Clinical Agentscited by 20
  18. 2023Augmenting Black-box LLMs with Medical Textbooks for Biomedical Question Answeringcited by 11
  19. 2023Integrating UMLS Knowledge into Large Language Models for Medical Question Answeringcited by 10
  20. 2024BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Sciencecited by 8
  21. 2024IryoNLP at MEDIQA-CORR 2024: Tackling the Medical Error Detection & Correction Task On the Shoulders of Medical Agentscited by 6
  22. 2024A Multi-Source Retrieval Question Answering Framework Based on RAGcited by 4
  23. 2024MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge Aggregationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 10; 2026: 3.

13 papers.

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  1. 2025RARE: Retrieval-Augmented Reasoning Modelingcited by 11
  2. 2025Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generationcited by 5
  3. 2025RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentationcited by 3
  4. 2025RAR2: Retrieval-Augmented Medical Reasoning via Thought-Driven Retrievalcited by 2
  5. 2025DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correctioncited by 2
  6. 2025EncouRAGe: Evaluating RAG Local, Fast, and Reliablecited by 2
  7. 2026FedMosaic: Federated Retrieval-Augmented Generation via Parametric Adapterscited by 2
  8. 2025POLYRAG: Integrating Polyviews into Retrieval-Augmented Generation for Medical Applicationscited by 1
  9. 2025HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Taskscited by 1
  10. 2025RAD: Towards Trustworthy Retrieval-Augmented Multi-modal Clinical Diagnosiscited by 1
  11. 2025Grounded by Experience: Generative Healthcare Prediction Augmented with Hierarchical Agentic Retrievalcited by 1
  12. 2026Not All Entities are Created Equal: A Dynamic Anonymization Framework for Privacy-Preserving RAGcited by 0
1 more
  1. 2026Reliable Multilingual Orthopedic Decision Support from Clinical Narratives: Language-Aware Adaptation and Verification-Guided Deferralcited by 0

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  2. 2026A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
  3. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
  4. 2025Optimizing Medical Question-Answering Systems: A Comparative Study of Fine-Tuned and Zero-Shot Large Language Models with RAG Framework
  5. 2025From Retrieval to Generation: Unifying External and Parametric Knowledge for Medical Question Answering
  6. 2025Leveraging Language Models and RAG for Efficient Knowledge Discovery in Clinical Environments
  7. 2025MIRA: A Novel Framework for Fusing Modalities in Medical RAG
  8. 2024A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models