mlsanityData to 14 Oct 2026

CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats

arXiv 2405.19538 ↗First posted 29 May 2024cs.CL cs.AI cs.CVcited by 106

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Cited by 32 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language models has sparked an increase in demands for sharing reports linked to CheXpert images, along with a growing interest among AI fairness researchers in obtaining demographic data. To address this, CheXpert Plus serves as a new collection of radiology data sources, made publicly available to enhance the scaling, performance, robustness, and fairness of models for all subsequent machine learning tasks in the field of radiology. CheXpert Plus is the largest text dataset publicly released in radiology, with a total of 36 million text tokens, including 13 million impression tokens. To the best of our knowledge, it represents the largest text de-identification effort in radiology, with almost 1 million PHI spans anonymized. It is only the second time that a large-scale English paired dataset has been released in radiology, thereby enabling, for the first time, cross-institution training at scale. All reports are paired with high-quality images in DICOM format, along with numerous image and patient metadata covering various clinical and socio-economic groups, as well as many pathology labels and RadGraph annotations. We hope this dataset will boost research for AI models that can further assist radiologists and help improve medical care. Data is available at the following URL: https://stanfordaimi.azurewebsites.net/datasets/5158c524-d3ab-4e02-96e9-6ee9efc110a1 Models are available at the following URL: https://github.com/Stanford-AIMI/chexpert-plus

Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

17 of 30 references.

References · 17

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  4. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  5. 2017ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseasescited by 972
  6. 2020Contrastive Learning of Medical Visual Representations from Paired Images and Textcited by 641
  7. 2023Med-Flamingo: a Multimodal Medical Few-shot Learnercited by 392
  8. 2020Generating Radiology Reports via Memory-driven Transformercited by 368
  9. 2020CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTcited by 337
  10. 2021RadGraph: Extracting Clinical Entities and Relations from Radiology Reportscited by 319
  11. 2019PadChest: A large chest x-ray image dataset with multi-label annotated reportscited by 309
  12. 2023Learning to Exploit Temporal Structure for Biomedical Vision-Language Processingcited by 211
5 more
  1. 2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 179
  2. 2022Improving the Factual Correctness of Radiology Report Generation with Semantic Rewardscited by 83
  3. 2020BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patientscited by 82
  4. 2024A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretationcited by 81
  5. 2023MAIRA-1: A specialised large multimodal model for radiology report generationcited by 74

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 9; 2025: 42; 2026: 55.

106 papers.

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  1. 2025Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoningcited by 230
  2. 2025A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontierscited by 37
  3. 2024CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Datasetcited by 35
  4. 2024PadChest-GR: A Bilingual Chest X-ray Dataset for Grounded Radiology Report Generationcited by 35
  5. 2025RadVLM: A Multitask Conversational Vision-Language Model for Radiologycited by 25
  6. 2025RadEval: A framework for radiology text evaluationcited by 22
  7. 2026MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Imagescited by 15
  8. 2025Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generationcited by 13
  9. 2025RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injectioncited by 13
  10. 2024R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generationcited by 12
  11. 2025Vision Language Models in Medicinecited by 12
  12. 2025UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysiscited by 12
38 more
  1. 2025Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translationcited by 8
  2. 2025Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoningcited by 8
  3. 2025RadAgents: Multimodal Agentic Reasoning for Chest X-ray Interpretation with Radiologist-like Workflowscited by 8
  4. 2024GPT-4V Cannot Generate Radiology Reports Yetcited by 7
  5. 2025Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Datacited by 7
  6. 2026MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMscited by 6
  7. 2026Overconfidence and Calibration in Medical VQA: Empirical Findings and Hallucination-Aware Mitigationcited by 6
  8. 2024Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generationcited by 5
  9. 2025UniVRSE: Unified Vision-conditioned Response Semantic Entropy for Hallucination Detection in Medical Vision-Language Modelscited by 5
  10. 2025A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generationcited by 5
  11. 2026A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretationcited by 5
  12. 2024RadPhi-3: Small Language Models for Radiologycited by 4
  13. 2024Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generationcited by 4
  14. 2025R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generationcited by 4
  15. 2025CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decodingcited by 4
  16. 2026Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verificationcited by 4
  17. 2025Subgroup Performance Analysis in Hidden Stratificationscited by 3
  18. 2025Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generationcited by 3
  19. 2025MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generationcited by 3
  20. 2025EMRRG: Efficient Fine-Tuning Pre-trained X-ray Mamba Networks for Radiology Report Generationcited by 3
  21. 2026Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervisioncited by 3
  22. 2026CWCD: Category-Wise Contrastive Decoding for Structured Medical Report Generationcited by 3
  23. 2026ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understandingcited by 3
  24. 2024Causality-Driven Audits of Model Robustnesscited by 2
  25. 2024LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generationcited by 2
  26. 2025Vision-Language Models for Automated Chest X-ray Interpretation: Leveraging ViT and GPT-2cited by 2
  27. 2025AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rayscited by 2
  28. 2025PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoningcited by 2
  29. 2025Limitations of Public Chest Radiography Datasets for Artificial Intelligence: Label Quality, Domain Shift, Bias and Evaluation Challengescited by 2
  30. 2025TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Modelscited by 2
  31. 2025Structure is Supervision: Multiview Masked Autoencoders for Radiologycited by 2
  32. 2025A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practicecited by 2
  33. 2026MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Educationcited by 2
  34. 2026A data- and compute-efficient chest X-ray foundation model beyond aggressive scalingcited by 2
  35. 2026Mask-Guided Attention Regulation for Anatomically Consistent Counterfactual CXR Synthesiscited by 2
  36. 2026Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reportscited by 2
  37. 2026CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretationcited by 2
  38. 2026Vision-language models for chest radiography do not always need the imagecited by 2

50 most cited of 106 All 106 (JSON)

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  1. 2026CheXpercept: A Benchmark for Evaluating Expert-Level Lesion Perception in Chest X-rays
  2. 2026CheXGround: Anatomical Region Tokens for Grounded Longitudinal Chest X-ray Interpretation
  3. 2024CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback
  4. 2025A Generative Foundation Model for Chest Radiography
  5. 2023Synthetically Enhanced: Unveiling Synthetic Data's Potential in Medical Imaging Research
  6. 2024CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting
  7. 2025CorBenchX: Large-Scale Chest X-Ray Error Dataset and Vision-Language Model Benchmark for Report Error Correction
  8. 2023CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images