# Quality Control for Radiology Report Generation Models via Auxiliary Auditing Components

arXiv: https://arxiv.org/abs/2407.21638 · HTML: https://mlsanity.com/paper/2407.21638/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation](https://mlsanity.com/paper/2302.09664/) (2023)
- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation](https://mlsanity.com/paper/2010.10042/) (2020)
- [Interactive and Explainable Region-guided Radiology Report Generation](https://mlsanity.com/paper/2304.08295/) (2023)
- [Clinically Accurate Chest X-Ray Report Generation](https://mlsanity.com/paper/1904.02633/) (2019)
- [RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting](https://mlsanity.com/paper/2107.02104/) (2021)
- [Uncertainty-Aware Evaluation for Vision-Language Models](https://mlsanity.com/paper/2402.14418/) (2024)
- [Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns](https://mlsanity.com/paper/2404.02370/) (2024)
- [Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting](https://mlsanity.com/paper/2308.15961/) (2023)
- [Exploring Multimodal Large Language Models for Radiology Report Error-checking](https://mlsanity.com/paper/2312.13103/) (2023)

## Built on by (most cited first, 2 of 2)

- [Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting](https://mlsanity.com/paper/2512.02814/) (2025)
- [Calibrated Confidence Expression for Radiology Report Generation](https://mlsanity.com/paper/2603.29492/) (2026)

## 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.