mlsanityData to 7 Oct 2026

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

arXiv 2407.05131 ↗First posted 6 Jul 2024cs.LG cs.AI cs.CLcited by 58

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts. Retrieval-Augmented Generation (RAG), which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges. First, limited retrieved contexts might not cover all necessary information, while excessive retrieval can introduce irrelevant and inaccurate references, interfering with the model's generation. Second, in cases where the model originally responds correctly, applying RAG can lead to an over-reliance on retrieved contexts, resulting in incorrect answers. To address these issues, we propose RULE, which consists of two components. First, we introduce a provably effective strategy for controlling factuality risk through the calibrated selection of the number of retrieved contexts. Second, based on samples where over-reliance on retrieved contexts led to errors, we curate a preference dataset to fine-tune the model, balancing its dependence on inherent knowledge and retrieved contexts for generation. We demonstrate the effectiveness of RULE on medical VQA and report generation tasks across three datasets, achieving an average improvement of 47.4% in factual accuracy. We publicly release our benchmark and code in https://github.com/richard-peng-xia/RULE.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 28 of its 48 references that are in the map. First posted on arXiv 6 Jul 2024.

Builds on

28 of 48 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day1 Jun

Other references · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  5. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  7. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  8. 2019Publicly Available Clinical BERT Embeddingscited by 995
  9. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decodingcited by 671
  10. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 590
  11. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocationcited by 468
  12. 2023Med-Flamingo: a Multimodal Medical Few-shot Learnercited by 387
15 more
  1. 2021Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Controlcited by 256
  2. 2023PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documentscited by 212
  3. 2024Aligning Modalities in Vision Large Language Models via Preference Fine-tuningcited by 197
  4. 2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 176
  5. 2024FairCLIP: Harnessing Fairness in Vision-Language Learningcited by 69
  6. 2024GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaborationcited by 33
  7. 2023RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-trainingcited by 31
  8. 2024OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understandingcited by 31
  9. 2023HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understandingcited by 21
  10. 2023NurViD: A Large Expert-Level Video Database for Nursing Procedure Activity Understandingcited by 20
  11. 2024Improving Medical Multi-modal Contrastive Learning with Expert Annotationscited by 16
  12. 2023LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognitioncited by 15
  13. 2024TP-DRSeg: Improving Diabetic Retinopathy Lesion Segmentation with Explicit Text-Prompts Assisted SAMcited by 13
  14. 2024Memory-based Cross-modal Semantic Alignment Network for Radiology Report Generationcited by 11
  15. 2024Generalizing to Unseen Domains in Diabetic Retinopathy with Disentangled Representationscited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 38; 2026: 12.

58 papers.

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  1. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Basescited by 402
  2. 2025A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyondcited by 131
  3. 2024MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Modelscited by 84
  4. 2025VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learningcited by 68
  5. 2025MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understandingcited by 66
  6. 2025MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoningcited by 37
  7. 2025M2IV: Towards Efficient and Fine-grained Multimodal In-Context Learning via Representation Engineeringcited by 34
  8. 2024MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Modelscited by 32
  9. 2025Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlookcited by 32
  10. 2024Beyond Text: Optimizing RAG with Multimodal Inputs for Industrial Applicationscited by 31
  11. 2025MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attackscited by 24
  12. 2025Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAGcited by 22
38 more
  1. 2024MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimizationcited by 21
  2. 2025FinSage: A Multi-aspect RAG System for Financial Filings Question Answeringcited by 18
  3. 2025Benchmarking Poisoning Attacks against Retrieval-Augmented Generationcited by 17
  4. 2025A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Taskscited by 15
  5. 2025Poisoned-MRAG: Knowledge Poisoning Attacks to Multimodal Retrieval Augmented Generationcited by 12
  6. 2025MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystackscited by 11
  7. 2025OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrievalcited by 10
  8. 2025One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Imagecited by 8
  9. 2025A Multimodal Multi-Agent Framework for Radiology Report Generationcited by 8
  10. 2025Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notescited by 7
  11. 2025MoMA: A Mixture-of-Multimodal-Agents Architecture for Enhancing Clinical Prediction Modellingcited by 7
  12. 2024Memory-Augmented Multimodal LLMs for Surgical VQA via Self-Contained Inquirycited by 6
  13. 2025Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitationcited by 6
  14. 2025Med-VCD: Mitigating Hallucination for Medical Large Vision Language Models through Visual Contrastive Decodingcited by 6
  15. 2025MOTOR: Multimodal Optimal Transport via Grounded Retrieval in Medical Visual Question Answeringcited by 5
  16. 2025Visual Alignment of Medical Vision-Language Models for Grounded Radiology Report Generationcited by 5
  17. 2025HoPE: Hybrid of Position Embedding for Long Context Vision-Language Modelscited by 4
  18. 2025MIRA: A Novel Framework for Fusing Modalities in Medical RAGcited by 4
  19. 2025Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Modelscited by 4
  20. 2024AlzheimerRAG: Multimodal Retrieval Augmented Generation for Clinical Use Cases using PubMed articlescited by 3
  21. 2025Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysiscited by 3
  22. 2025MrM: Black-Box Membership Inference Attacks against Multimodal RAG Systemscited by 2
  23. 2025On the Risk of Misleading Reports: Diagnosing Textual Biases in Multimodal Clinical AIcited by 2
  24. 2025Empowering Multimodal LLMs with External Tools: A Comprehensive Surveycited by 2
  25. 2025BudgetLeak: Membership Inference Attacks on RAG Systems via the Generation Budget Side Channelcited by 2
  26. 2025MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answeringcited by 2
  27. 2024GPT Sonograpy: Hand Gesture Decoding from Forearm Ultrasound Images via VLMcited by 1
  28. 2025Safeguarding Multimodal Knowledge Copyright in the RAG-as-a-Service Environmentcited by 1
  29. 2025A Multi-Agent System for Complex Reasoning in Radiology Visual Question Answeringcited by 1
  30. 2025Med-GRIM: Enhanced Zero-Shot Medical VQA using prompt-embedded Multimodal Graph RAGcited by 1
  31. 2025HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Taskscited by 1
  32. 2025MedFact-R1: Towards Factual Medical Reasoning via Pseudo-Label Augmentationcited by 1
  33. 2025Fine-Tuning MedGemma for Clinical Captioning to Enhance Multimodal RAG over Malaysia CPGscited by 1
  34. 2025Med-VRAgent: A Framework for Medical Visual Reasoning-Enhanced Agentscited by 1
  35. 2026Sparse Spectral LoRA: Routed Experts for Medical VLMscited by 1
  36. 2026Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agentscited by 1
  37. 2026The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomycited by 1
  38. 2026Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generationcited by 0

50 most cited of 58 All 58 (JSON)

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  2. 2024Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology
  3. 2025Rethinking Retrieval-Augmented Generation for Medicine: A Large-Scale, Systematic Expert Evaluation and Practical Insights
  4. 2025mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation
  5. 2025Retrieval-Augmented Generation in Medicine: A Scoping Review of Technical Implementations, Clinical Applications, and Ethical Considerations
  6. 2025RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance
  7. 2024Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report
  8. 2025Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines