mlsanityData to 7 Oct 2026

Retrieval Augmented Chest X-Ray Report Generation using OpenAI GPT models

arXiv 2305.03660 ↗First posted 5 May 2023cs.CL cs.AI cs.IRcited by 27

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

Abstract (arXiv)

We propose Retrieval Augmented Generation (RAG) as an approach for automated radiology report writing that leverages multimodally aligned embeddings from a contrastively pretrained vision language model for retrieval of relevant candidate radiology text for an input radiology image and a general domain generative model like OpenAI text-davinci-003, gpt-3.5-turbo and gpt-4 for report generation using the relevant radiology text retrieved. This approach keeps hallucinated generations under check and provides capabilities to generate report content in the format we desire leveraging the instruction following capabilities of these generative models. Our approach achieves better clinical metrics with a BERTScore of 0.2865 (Delta+ 25.88%) and Semb score of 0.4026 (Delta+ 6.31%). Our approach can be broadly relevant for different clinical settings as it allows to augment the automated radiology report generation process with content relevant for that setting while also having the ability to inject user intents and requirements in the prompts as part of the report generation process to modulate the content and format of the generated reports as applicable for that clinical setting.

Key facts. Cited by 27 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 10 of its 15 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

10 of 15 references.

References · 10

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,612
  4. 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 590
  5. 2020Generating Radiology Reports via Memory-driven Transformercited by 363
  6. 2022Making the Most of Text Semantics to Improve Biomedical Vision--Language Processingcited by 336
  7. 2020CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTcited by 329
  8. 2021RadGraph: Extracting Clinical Entities and Relations from Radiology Reportscited by 310
  9. 2020Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generationcited by 139
  10. 2022Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priorscited by 48

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 7; 2025: 13; 2026: 4.

27 papers.

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  1. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  2. 2024Vision-Language Models for Medical Report Generation and Visual Question Answering: A Reviewcited by 121
  3. 2023Foundational Models Defining a New Era in Vision: A Survey and Outlookcited by 109
  4. 2025Visual Large Language Models for Generalized and Specialized Applicationscited by 35
  5. 2023Exploring the Boundaries of GPT-4 in Radiologycited by 31
  6. 2024A Survey of Medical Vision-and-Language Applications and Their Techniquescited by 11
  7. 2025RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injectioncited by 11
  8. 2025MedGUIDE: Benchmarking Clinical Decision-Making in Large Language Modelscited by 10
  9. 2024Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAGcited by 9
  10. 2025A Multimodal Multi-Agent Framework for Radiology Report Generationcited by 8
  11. 2024Direct Preference Optimization for Suppressing Hallucinated Prior Exams in Radiology Report Generationcited by 7
  12. 2024LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generationcited by 2
15 more
  1. 2025Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reportscited by 2
  2. 2024Evaluation of Language Models in the Medical Context Under Resource-Constrained Settingscited by 1
  3. 2024Designing a Robust Radiology Report Generation Systemcited by 1
  4. 2025RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extractioncited by 1
  5. 2025Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reportscited by 1
  6. 2025HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Taskscited by 1
  7. 2025Analysing Environmental Efficiency in AI for X-Ray Diagnosiscited by 1
  8. 2025Georeferencing complex relative locality descriptions with large language modelscited by 1
  9. 2026RAGPerf: An End-to-End Benchmarking Framework for Retrieval-Augmented Generation Systemscited by 1
  10. 2026MedScribe: Clinically Grounded CT Reporting through Agentic Workflowscited by 1
  11. 2025LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuningcited by 0
  12. 2025Augmented Vision-Language Models: A Systematic Reviewcited by 0
  13. 2025Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literaturecited by 0
  14. 2026Cross-Modal Clinical Knowledge Integration for Mammography Report Generationcited by 0
  15. 2026MAC-RRG: Iterative Multi-Agent Collaboration for X-ray Radiology Report Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generation
  2. 2024SERPENT-VLM : Self-Refining Radiology Report Generation Using Vision Language Models
  3. 2024Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
  4. 2026Concept-Enhanced Multimodal RAG: Towards Interpretable and Accurate Radiology Report Generation
  5. 2023Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
  6. 2023MAIRA-1: A specialised large multimodal model for radiology report generation
  7. 2025CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models
  8. 2025Any-to-Any Vision-Language Model for Multimodal X-ray Imaging and Radiological Report Generation