mlsanityData to 7 Oct 2026

FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models

arXiv 2411.18672 ↗First posted 27 Nov 2024cs.CVcited by 11

Not on the map. Why?

Abstract (arXiv)

Medical vision-language models often struggle with generating accurate quantitative measurements in radiology reports, leading to hallucinations that undermine clinical reliability. We introduce FactCheXcker, a modular framework that de-hallucinates radiology report measurements by leveraging an improved query-code-update paradigm. Specifically, FactCheXcker employs specialized modules and the code generation capabilities of large language models to solve measurement queries generated based on the original report. After extracting measurable findings, the results are incorporated into an updated report. We evaluate FactCheXcker on endotracheal tube placement, which accounts for an average of 78% of report measurements, using the MIMIC-CXR dataset and 11 medical report-generation models. Our results show that FactCheXcker significantly reduces hallucinations, improves measurement precision, and maintains the quality of the original reports. Specifically, FactCheXcker improves the performance of 10/11 models and achieves an average improvement of 135.0% in reducing measurement hallucinations measured by mean absolute error. Code is available at https://github.com/rajpurkarlab/FactCheXcker.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 27 Nov 2024.

Builds on

34 of 51 references.

References · 34

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2024GPT-4o System Cardcited by 4,467
  6. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  8. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,696
  9. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  10. 2021Swin Transformer V2: Scaling Up Capacity and Resolutioncited by 1,215
  11. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  12. 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
22 more
  1. 2018Object Hallucination in Image Captioningcited by 661
  2. 2023ViperGPT: Visual Inference via Python Execution for Reasoningcited by 591
  3. 2023API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMscited by 574
  4. 2023Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuningcited by 439
  5. 2024Hallucination of Multimodal Large Language Models: A Surveycited by 380
  6. 2023Towards Generalist Biomedical AIcited by 286
  7. 2023Detecting and Preventing Hallucinations in Large Vision Language Modelscited by 254
  8. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  9. 2024Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decodingcited by 198
  10. 2023TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIscited by 184
  11. 2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 176
  12. 2023Fact-Checking Complex Claims with Program-Guided Reasoningcited by 146
  13. 2024MAIRA-2: Grounded Radiology Report Generationcited by 140
  14. 2023Interactive and Explainable Region-guided Radiology Report Generationcited by 139
  15. 2021TorchXRayVision: A library of chest X-ray datasets and modelscited by 131
  16. 2021Chest ImaGenome Dataset for Clinical Reasoningcited by 89
  17. 2023Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Modelscited by 80
  18. 2023MAIRA-1: A specialised large multimodal model for radiology report generationcited by 72
  19. 2023LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generationcited by 57
  20. 2024Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiologycited by 45
  21. 2024Automated Radiology Report Generation: A Review of Recent Advancescited by 43
  22. 2024Uncovering Knowledge Gaps in Radiology Report Generation Models through Knowledge Graphscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 7.

11 papers.

  1. 2025A Survey of Multimodal Hallucination Evaluation and Detectioncited by 26
  2. 2026Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steeringcited by 8
  3. 2025Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalitiescited by 6
  4. 2025Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learningcited by 5
  5. 2026ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialoguecited by 4
  6. 2025Analysing Environmental Efficiency in AI for X-Ray Diagnosiscited by 1
  7. 2026SAKED: Mitigating Hallucination in Large Vision-Language Models via Stability-Aware Knowledge Enhanced Decodingcited by 1
  8. 2026Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generationcited by 1
  9. 2026Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generationcited by 1
  10. 2026Best-of-Evidence: Best-of-N Selection under Partial Verificationcited by 0
  11. 2026From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports
  2. 2026HalluCXR: Benchmarking and Mitigating Hallucinations in Medical Vision-Language Models for Chest Radiograph Interpretation
  3. 2025Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports
  4. 2026When Retrieval Hurts: Measuring and Explaining Retrieval-Induced Hallucination in Chest X-ray Report Generation
  5. 2025CorBenchX: Large-Scale Chest X-Ray Error Dataset and Vision-Language Model Benchmark for Report Error Correction
  6. 2025A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation
  7. 2026Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering
  8. 2024RadFlag: A Black-Box Hallucination Detection Method for Medical Vision Language Models