# Toward expanding the scope of radiology report summarization to multiple anatomies and modalities

arXiv: https://arxiv.org/abs/2211.08584 · HTML: https://mlsanity.com/paper/2211.08584/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)
- [Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation](https://mlsanity.com/paper/2010.10042/) (2020)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model](https://mlsanity.com/paper/2204.03905/) (2022)
- [Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards](https://mlsanity.com/paper/2210.12186/) (2022)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [Graph Enhanced Contrastive Learning for Radiology Findings Summarization](https://mlsanity.com/paper/2204.00203/) (2022)
- [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021)
- [Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization](https://mlsanity.com/paper/2203.08257/) (2022)

## Built on by (most cited first, 14 of 14)

- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [A dataset and benchmark for hospital course summarization with adapted large language models](https://mlsanity.com/paper/2403.05720/) (2024)
- [FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization](https://mlsanity.com/paper/2303.08335/) (2023)
- [SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrieval](https://mlsanity.com/paper/2401.02369/) (2024)
- [Extracting and Encoding: Leveraging Large Language Models and Medical Knowledge to Enhance Radiological Text Representation](https://mlsanity.com/paper/2407.01948/) (2024)
- [Automatic Personalized Impression Generation for PET Reports Using Large Language Models](https://mlsanity.com/paper/2309.10066/) (2023)
- [Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis](https://mlsanity.com/paper/2307.12803/) (2023)
- [Improving Expert Radiology Report Summarization by Prompting Large Language Models with a Layperson Summary](https://mlsanity.com/paper/2406.14500/) (2024)
- [Holistic Artificial Intelligence in Medicine; improved performance and explainability](https://mlsanity.com/paper/2507.00205/) (2025)
- [Training-Free Adaptation of New-Generation LLMs using Legacy Clinical Models](https://mlsanity.com/paper/2601.03423/) (2026)
- [Perfecting Human-AI Interaction at Clinical Scale. Turning Production Signals into Safer, More Human Conversations](https://mlsanity.com/paper/2603.29893/) (2026)
- [Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization](https://mlsanity.com/paper/2603.29901/) (2026)

## Abstract (arXiv)

Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression section) that highlights the key observations and conclusions of the radiology study. However, RRS currently faces essential limitations.First, many prior studies conduct experiments on private datasets, preventing reproduction of results and fair comparisons across different systems and solutions. Second, most prior approaches are evaluated solely on chest X-rays. To address these limitations, we propose a dataset (MIMIC-RRS) involving three new modalities and seven new anatomies based on the MIMIC-III and MIMIC-CXR datasets. We then conduct extensive experiments to evaluate the performance of models both within and across modality-anatomy pairs in MIMIC-RRS. In addition, we evaluate their clinical efficacy via RadGraph, a factual correctness metric.