mlsanityData to 14 Oct 2026

Large Language Model Aided Birt-Hogg-Dube Syndrome Diagnosis with Multimodal Retrieval-Augmented Generation

arXiv 2511.19834 ↗First posted 25 Nov 2025cs.CVcited by 0

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

Abstract (arXiv)

Deep learning methods face dual challenges of limited clinical samples and low inter-class differentiation among Diffuse Cystic Lung Diseases (DCLDs) in advancing Birt-Hogg-Dube syndrome (BHD) diagnosis via Computed Tomography (CT) imaging. While Multimodal Large Language Models (MLLMs) demonstrate diagnostic potential fo such rare diseases, the absence of domain-specific knowledge and referable radiological features intensify hallucination risks. To address this problem, we propose BHD-RAG, a multimodal retrieval-augmented generation framework that integrates DCLD-specific expertise and clinical precedents with MLLMs to improve BHD diagnostic accuracy. BHDRAG employs: (1) a specialized agent generating imaging manifestation descriptions of CT images to construct a multimodal corpus of DCLDs cases. (2) a cosine similarity-based retriever pinpointing relevant imagedescription pairs for query images, and (3) an MLLM synthesizing retrieved evidence with imaging data for diagnosis. BHD-RAG is validated on the dataset involving four types of DCLDs, achieving superior accuracy and generating evidence-based descriptions closely aligned with expert insights.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 30 references that are in the map. First posted on arXiv 25 Nov 2025.

Builds on

18 of 30 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation8 Aug
  2. 2024Improving Retrieval-Augmented Generation in Medicine with Iterative Follow-up Questions1 Aug

Other references · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  5. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  8. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
  9. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
  10. 2018CosFace: Large Margin Cosine Loss for Deep Face Recognitioncited by 971
  11. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  12. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 329
4 more
  1. 2019Med3D: Transfer Learning for 3D Medical Image Analysiscited by 288
  2. 2024The Power of Noise: Redefining Retrieval for RAG Systemscited by 281
  3. 2024Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learningcited by 47
  4. 2021Deep Metric Learning for Few-Shot Image Classification: A Review of Recent Developmentscited by 23

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  2. 2025CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding
  3. 2025LVLM-Aware Multimodal Retrieval for RAG-Based Medical Diagnosis with General-Purpose Models
  4. 2024MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models
  5. 2025X-Ray-CoT: Interpretable Chest X-ray Diagnosis with Vision-Language Models via Chain-of-Thought Reasoning
  6. 2024Evaluating LLM -- Generated Multimodal Diagnosis from Medical Images and Symptom Analysis
  7. 2025ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images
  8. 2025ICA-RAG: Information Completeness Guided Adaptive Retrieval-Augmented Generation for Disease Diagnosis