# Automated Radiology Report Generation: A Review of Recent Advances

arXiv: https://arxiv.org/abs/2405.10842 · HTML: https://mlsanity.com/paper/2405.10842/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 87 of its 155 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 30 of 87)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (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)
- [Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing](https://mlsanity.com/paper/2204.09817/) (2022)
- [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)

## Built on by (most cited first, 30 of 43)

- [MAIRA-2: Grounded Radiology Report Generation](https://mlsanity.com/paper/2406.04449/) (2024)
- [Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation](https://mlsanity.com/paper/2502.20056/) (2025)
- [Libra: Leveraging Temporal Images for Biomedical Radiology Analysis](https://mlsanity.com/paper/2411.19378/) (2024)
- [SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation](https://mlsanity.com/paper/2508.15658/) (2025)
- [FactCheXcker: Mitigating Measurement Hallucinations in Chest X-ray Report Generation Models](https://mlsanity.com/paper/2411.18672/) (2024)
- [ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports](https://mlsanity.com/paper/2409.10829/) (2024)
- [DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation](https://mlsanity.com/paper/2505.03401/) (2025)
- [Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning](https://mlsanity.com/paper/2509.19090/) (2025)
- [MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models](https://mlsanity.com/paper/2411.11362/) (2024)
- [Evaluating and Mitigating Bias in AI-Based Medical Text Generation](https://mlsanity.com/paper/2504.17279/) (2025)
- [Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation](https://mlsanity.com/paper/2504.18453/) (2025)
- [A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation](https://mlsanity.com/paper/2510.12444/) (2025)
- [HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation](https://mlsanity.com/paper/2409.13038/) (2024)
- [LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation](https://mlsanity.com/paper/2411.16523/) (2024)
- [Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives](https://mlsanity.com/paper/2412.04812/) (2024)
- [Enhancing Surgical Documentation through Multimodal Visual-Temporal Transformers and Generative AI](https://mlsanity.com/paper/2504.19918/) (2025)
- [Automated Structured Radiology Report Generation with Rich Clinical Context](https://mlsanity.com/paper/2510.00428/) (2025)
- [Resolution scaling governs DINOv3 transfer performance in chest radiograph classification](https://mlsanity.com/paper/2510.07191/) (2025)
- [Ultrasound Report Generation with Multimodal Large Language Models for Standardized Texts](https://mlsanity.com/paper/2505.08838/) (2025)
- [MedErr-CT: A Visual Question Answering Benchmark for Identifying and Correcting Errors in CT Reports](https://mlsanity.com/paper/2506.19217/) (2025)
- [From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization](https://mlsanity.com/paper/2601.16397/) (2026)
- [Personalized Longitudinal Medical Report Generation via Temporally-Aware Federated Adaptation](https://mlsanity.com/paper/2602.19668/) (2026)
- [SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation](https://mlsanity.com/paper/2605.24371/) (2026)
- [Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generation](https://mlsanity.com/paper/2605.27194/) (2026)
- [LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning](https://mlsanity.com/paper/2506.03178/) (2025)
- [An Iterative LLM Framework for SIBT utilizing RAG-based Adaptive Weight Optimization](https://mlsanity.com/paper/2509.08407/) (2025)
- [AI-Driven Radiology Report Generation for Traumatic Brain Injuries](https://mlsanity.com/paper/2510.08498/) (2025)
- [Closing the Performance Gap Between AI and Radiologists in Chest X-Ray Reporting](https://mlsanity.com/paper/2511.21735/) (2025)
- [Scaling Down to Scale Up: Towards Operationally-Efficient and Deployable Clinical Models via Cross-Modal Low-Rank Adaptation for Medical Vision-Language Models](https://mlsanity.com/paper/2512.00597/) (2025)
- [A Generalist Foundation Model for Total-body PET/CT Enables Diagnostic Reporting and System-wide Metabolic Profiling](https://mlsanity.com/paper/2601.12820/) (2026)

## Abstract (arXiv)

Increasing demands on medical imaging departments are taking a toll on the radiologist's ability to deliver timely and accurate reports. Recent technological advances in artificial intelligence have demonstrated great potential for automatic radiology report generation (ARRG), sparking an explosion of research. This survey paper conducts a methodological review of contemporary ARRG approaches by way of (i) assessing datasets based on characteristics, such as availability, size, and adoption rate, (ii) examining deep learning training methods, such as contrastive learning and reinforcement learning, (iii) exploring state-of-the-art model architectures, including variations of CNN and transformer models, (iv) outlining techniques integrating clinical knowledge through multimodal inputs and knowledge graphs, and (v) scrutinising current model evaluation techniques, including commonly applied NLP metrics and qualitative clinical reviews. Furthermore, the quantitative results of the reviewed models are analysed, where the top performing models are examined to seek further insights. Finally, potential new directions are highlighted, with the adoption of additional datasets from other radiological modalities and improved evaluation methods predicted as important areas of future development.