mlsanityData to 7 Oct 2026

Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components

arXiv 2407.21638 ↗First posted 31 Jul 2024cs.AI cs.CVcited by 2

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Abstract (arXiv)

Automation of medical image interpretation could alleviate bottlenecks in diagnostic workflows, and has become of particular interest in recent years due to advancements in natural language processing. Great strides have been made towards automated radiology report generation via AI, yet ensuring clinical accuracy in generated reports is a significant challenge, hindering deployment of such methods in clinical practice. In this work we propose a quality control framework for assessing the reliability of AI-generated radiology reports with respect to semantics of diagnostic importance using modular auxiliary auditing components (AC). Evaluating our pipeline on the MIMIC-CXR dataset, our findings show that incorporating ACs in the form of disease-classifiers can enable auditing that identifies more reliable reports, resulting in higher F1 scores compared to unfiltered generated reports. Additionally, leveraging the confidence of the AC labels further improves the audit's effectiveness.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

18 of 30 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,337
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  4. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,287
  5. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  6. 2023Med-Flamingo: a Multimodal Medical Few-shot Learnercited by 387
  7. 2020Generating Radiology Reports via Memory-driven Transformercited by 363
  8. 2019Meshed-Memory Transformer for Image Captioningcited by 343
  9. 2020CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTcited by 329
  10. 2022Cross-modal Memory Networks for Radiology Report Generationcited by 183
  11. 2020Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generationcited by 139
  12. 2023Interactive and Explainable Region-guided Radiology Report Generationcited by 139
6 more
  1. 2019Clinically Accurate Chest X-Ray Report Generationcited by 138
  2. 2021RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reportingcited by 34
  3. 2024Uncertainty-Aware Evaluation for Vision-Language Modelscited by 28
  4. 2024Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patternscited by 8
  5. 2023Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reportingcited by 6
  6. 2023Exploring Multimodal Large Language Models for Radiology Report Error-checkingcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reportingcited by 2
  2. 2026Calibrated Confidence Expression for Radiology Report Generationcited by 0