# Automated Structured Radiology Report Generation

arXiv: https://arxiv.org/abs/2505.24223 · HTML: https://mlsanity.com/paper/2505.24223/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 4 of 4)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation](https://mlsanity.com/paper/2111.04318/) (2021)
- [MedVersa: A Generalist Foundation Model for Medical Image Interpretation](https://mlsanity.com/paper/2405.07988/) (2024)

## Built on by (most cited first, 15 of 15)

- [Evaluating Reasoning Faithfulness in Medical Vision-Language Models using Multimodal Perturbations](https://mlsanity.com/paper/2510.11196/) (2025)
- [Scaling medical imaging report generation with multimodal reinforcement learning](https://mlsanity.com/paper/2601.17151/) (2026)
- [CWCD: Category-Wise Contrastive Decoding for Structured Medical Report Generation](https://mlsanity.com/paper/2604.10410/) (2026)
- [Enhancing Fine-Grained Spatial Grounding in 3D CT Report Generation via Discriminative Guidance](https://mlsanity.com/paper/2604.10437/) (2026)
- [Automated Structured Radiology Report Generation with Rich Clinical Context](https://mlsanity.com/paper/2510.00428/) (2025)
- [Towards Better Aligned LLM Judges for Radiology Report Evaluation across Modalities and Anatomies](https://mlsanity.com/paper/2604.03376/) (2026)
- [Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation](https://mlsanity.com/paper/2603.16876/) (2026)
- [Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays](https://mlsanity.com/paper/2603.26049/) (2026)
- [Prototype-Based Knowledge Guidance for Fine-Grained Structured Radiology Reporting](https://mlsanity.com/paper/2603.11938/) (2026)
- [Ran Score: a LLM-based Evaluation Score for Radiology Report Generation](https://mlsanity.com/paper/2603.22935/) (2026)
- [CXRMate-2: Structured Multimodal Temporal Embeddings and Tractable Reinforcement Learning for Clinically Acceptable Chest X-ray Radiology Report Generation](https://mlsanity.com/paper/2604.18967/) (2026)
- [The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2605.05594/) (2026)
- [Decomposing Whole Slide Image Report Generation with Graph-Constrained Multiple Instance Learning Workflows](https://mlsanity.com/paper/2608.15353/) (2026)
- [Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation](https://mlsanity.com/paper/2609.19093/) (2026)
- [How Medical VLMs Underutilize Their Vision Encoders: A Dermatology Perspective](https://mlsanity.com/paper/2609.36557/) (2026)

## Abstract (arXiv)

Automated radiology report generation from chest X-ray (CXR) images has the potential to improve clinical efficiency and reduce radiologists' workload. However, most datasets, including the publicly available MIMIC-CXR and CheXpert Plus, consist entirely of free-form reports, which are inherently variable and unstructured. This variability poses challenges for both generation and evaluation: existing models struggle to produce consistent, clinically meaningful reports, and standard evaluation metrics fail to capture the nuances of radiological interpretation. To address this, we introduce Structured Radiology Report Generation (SRRG), a new task that reformulates free-text radiology reports into a standardized format, ensuring clarity, consistency, and structured clinical reporting. We create a novel dataset by restructuring reports using large language models (LLMs) following strict structured reporting desiderata. Additionally, we introduce SRR-BERT, a fine-grained disease classification model trained on 55 labels, enabling more precise and clinically informed evaluation of structured reports. To assess report quality, we propose F1-SRR-BERT, a metric that leverages SRR-BERT's hierarchical disease taxonomy to bridge the gap between free-text variability and structured clinical reporting. We validate our dataset through a reader study conducted by five board-certified radiologists and extensive benchmarking experiments.