Looking at Radiology Report Generation through a Causal Lens: A Survey (2026.acl-long)
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| Challenge: | Existing surveys on RRG emphasize deep learning while overlooking the critical role of causality. |
| Approach: | They propose to analyze biases across the RRG pipeline and formalize it as a causal modeling problem and review representative causal techniques from the literature. |
| Outcome: | The proposed model can mitigate biases and yield fair, reliable systems with clinically meaningful outputs. |
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Kun Zhao, Chenghao Xiao, Sixing Yan, Haoteng Tang, William K. Cheung, Noura Al Moubayed, Liang Zhan, Chenghua Lin
| Challenge: | Technical language and templated nature of professional reports hinder patient comprehension and allow models to artificially boost lexical metrics such as BLEU by reproducing common report patterns. |
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Toward Expanding the Scope of Radiology Report Summarization to Multiple Anatomies and Modalities (2023.acl-short)
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| Challenge: | Existing studies are limited to a single modality and a chest X-ray, making it difficult to replicate results or compare approaches. |
| Approach: | They propose a dataset to generate an impression section of a radiology report . they propose to use three new modalities and seven new anatomies to evaluate their models . |
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Divide and Conquer Radiology Report Generation via Observation Level Fine-grained Pretraining and Prompt Tuning (2024.emnlp-main)
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| Challenge: | Recent advances in image captioning and vision-language pretraining have made it difficult for radiologists to generate coherent and accurate reports. |
| Approach: | They propose a model which breaks down full-text radiology reports into concise observation descriptions and encodes observation predictions into a decoding stage. |
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RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning (2023.findings-emnlp)
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| Challenge: | Recent studies have focused on producing concise observations while neglecting the precise attributes that determine the severity of diseases. |
| Approach: | They propose a model that generates precise radiology reports via dynamic disease progression reasoning by combining historical and spatiotemporal information. |
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Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generation (2025.findings-naacl)
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| Challenge: | Radiology report generation has shown great potential in assisting radiologists . generative medical Vision Large Language Models (VLLMs) are prone to hallucinations and can produce inaccurate diagnostic information. |
| Approach: | They propose a framework that provides both report-level and sentence-level uncertainties. |
| Outcome: | The proposed method improves factuality scores by 10% by rejecting 20% of reports on the MIMIC-CXR dataset. |
ReEvalMed: Rethinking Medical Report Evaluation by Aligning Metrics with Real-World Clinical Judgment (2025.emnlp-main)
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| Challenge: | Automatically generated radiology reports often receive high scores from existing evaluation metrics but fail to earn clinicians’ trust. |
| Approach: | They propose a meta-evaluation framework that uses criteria spanning discrimination, robustness, and monotonicity to evaluate existing metrics. |
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Multimodal Generation of Radiology Reports using Knowledge-Grounded Extraction of Entities and Relations (2022.aacl-main)
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Francesco Dalla Serra, William Clackett, Hamish MacKinnon, Chaoyang Wang, Fani Deligianni, Jeff Dalton, Alison Q. O’Neil
| Challenge: | Existing approaches to generate text radiology reports are prone to errors and poor clinical accuracy. |
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Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards (2022.findings-emnlp)
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Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen, Emily Tsai, Omar Almusa, Curtis Langlotz
| Challenge: | Neural image-to-text radiology report generation systems have been successful on NLG metrics, but they are not factually complete or consistent due to inadequate training and evaluation. |
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CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback (2025.acl-long)
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Dennis Hein, Zhihong Chen, Sophie Ostmeier, Justin Xu, Maya Varma, Eduardo Pontes Reis, Arne Edward Michalson Md, Christian Bluethgen, Hyun Joo Shin, Curtis Langlotz, Akshay S Chaudhari
| Challenge: | Radiologists are a crucial role in translating medical images into actionable reports . however, the field faces staffing shortages and increasing workloads . |
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RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction (2026.findings-acl)
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| Challenge: | Existing MLLMs are computationally expensive and may produce hallucinated content . RA-RRG uses large language models to generate radiology reports . |
| Approach: | They propose a retrieval-augmented RRG framework that combines multimodal retrieval with large language models to generate radiology reports. |
| Outcome: | RA-RRG uses large language models to generate radiology reports . it suppresses hallucinations while maintaining strong report generation performance . |