# Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting

arXiv: https://arxiv.org/abs/2310.17811 · HTML: https://mlsanity.com/paper/2310.17811/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [On the Automatic Generation of Medical Imaging Reports](https://mlsanity.com/paper/1711.08195/) (2017)
- [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://mlsanity.com/paper/2106.06963/) (2021)
- [When Radiology Report Generation Meets Knowledge Graph](https://mlsanity.com/paper/2002.08277/) (2020)
- [TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays](https://mlsanity.com/paper/1801.04334/) (2018)
- [Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation](https://mlsanity.com/paper/1903.10122/) (2019)
- [Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards](https://mlsanity.com/paper/2210.12186/) (2022)
- [Knowledge Matters: Radiology Report Generation with General and Specific Knowledge](https://mlsanity.com/paper/2112.15009/) (2021)
- [Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment](https://mlsanity.com/paper/1907.09085/) (2019)
- [Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation](https://mlsanity.com/paper/1603.08486/) (2016)
- [A Medical Semantic-Assisted Transformer for Radiographic Report Generation](https://mlsanity.com/paper/2208.10358/) (2022)
- [Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation](https://mlsanity.com/paper/2006.03744/) (2020)
- [Automated Generation of Accurate & Fluent Medical X-ray Reports](https://mlsanity.com/paper/2108.12126/) (2021)

## Built on by (most cited first, 29 of 29)

- [MAIRA-1: A specialised large multimodal model for radiology report generation](https://mlsanity.com/paper/2311.13668/) (2023)
- [Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation](https://mlsanity.com/paper/2502.20056/) (2025)
- [Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation](https://mlsanity.com/paper/2407.02235/) (2024)
- [An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation](https://mlsanity.com/paper/2410.03334/) (2024)
- [SurGen: Text-Guided Diffusion Model for Surgical Video Generation](https://mlsanity.com/paper/2408.14028/) (2024)
- [Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation](https://mlsanity.com/paper/2405.14905/) (2024)
- [RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection](https://mlsanity.com/paper/2505.14318/) (2025)
- [Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation](https://mlsanity.com/paper/2311.18260/) (2023)
- [Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation](https://mlsanity.com/paper/2405.09586/) (2024)
- [X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation](https://mlsanity.com/paper/2406.17911/) (2024)
- [Direct Preference Optimization for Suppressing Hallucinated Prior Exams in Radiology Report Generation](https://mlsanity.com/paper/2406.06496/) (2024)
- [Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling](https://mlsanity.com/paper/2409.08788/) (2024)
- [Best Practices for Large Language Models in Radiology](https://mlsanity.com/paper/2412.01233/) (2024)
- [SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning](https://mlsanity.com/paper/2506.21355/) (2025)
- [EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge](https://mlsanity.com/paper/2411.10224/) (2024)
- [Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation](https://mlsanity.com/paper/2412.04954/) (2024)
- [RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction](https://mlsanity.com/paper/2504.07415/) (2025)
- [MEPNet: Medical Entity-balanced Prompting Network for Brain CT Report Generation](https://mlsanity.com/paper/2503.17784/) (2025)
- [ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation](https://mlsanity.com/paper/2402.12844/) (2024)
- [A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice](https://mlsanity.com/paper/2512.20344/) (2025)
- [Opportunities and challenges in the application of large artificial intelligence models in radiology](https://mlsanity.com/paper/2403.16112/) (2024)
- [Improving Expert Radiology Report Summarization by Prompting Large Language Models with a Layperson Summary](https://mlsanity.com/paper/2406.14500/) (2024)
- [Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation](https://mlsanity.com/paper/2603.16876/) (2026)
- [MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting](https://mlsanity.com/paper/2412.02971/) (2024)
- [Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning](https://mlsanity.com/paper/2605.14717/) (2026)
- [Learning to See Locally and Align Clinically with Pathology Semantics for Radiology Report Generation](https://mlsanity.com/paper/2608.00279/) (2026)
- [SHIFT-M3: Pre-fusion Alignment-based Consistency Screening for Multimodal ECG Record Integrity](https://mlsanity.com/paper/2609.13874/) (2026)
- [Corpus Characterization and Inverse Constitutional Fine-Tuning for Style-Aware Radiology Reports](https://mlsanity.com/paper/2609.14226/) (2026)
- [CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation](https://mlsanity.com/paper/2610.13059/) (2026)

## Abstract (arXiv)

Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generating a fully-fledged report from an image. However, this conflates the content of the report (e.g., findings and their attributes) with its style (e.g., format and choice of words), which can lead to clinically inaccurate reports. To address this, we propose a two-step approach for radiology report generation. First, we extract the content from an image; then, we verbalize the extracted content into a report that matches the style of a specific radiologist. For this, we leverage RadGraph -- a graph representation of reports -- together with large language models (LLMs). In our quantitative evaluations, we find that our approach leads to beneficial performance. Our human evaluation with clinical raters highlights that the AI-generated reports are indistinguishably tailored to the style of individual radiologist despite leveraging only a few examples as context.