Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
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Benjamin Yan, Ruochen Liu, David Kuo, Subathra Adithan, Eduardo Reis, Stephen Kwak, Vasantha Venugopal, Chloe O’Connell, Agustina Saenz, Pranav Rajpurkar, Michael Moor. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.
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Related works have instrumented quantitative (completeness, correctness, conciseness) and qualitative (content, language, structure) measurements to distinguish synthesized clinical reports from human reports.
“Therefore, related works have instrumented quantitative (completeness, correctness, conciseness) and qualitative (content, language, structure) measurements to dissect the riveting characters that distinguish synthesized clinical reports from human reports46–49.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [47]
The significance of the medical report writing style was also highlighted in an independent prompting study.
“The significance of the medical report writing style was also highlighted in an independent prompting study47.”
From Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation (2025) · cites this paper as [47]
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- Authors
- Yan, B., Liu, R., Kuo, D., Adithan, S., Reis, E., Kwak, S., Venugopal, V., O’Connell, C., Saenz, A., Rajpurkar, P., & Moor, M.
- Affiliations
- Stanford University (US)
- Jawaharlal Institute of Post Graduate Medical Education and Research (IN)
- Johns Hopkins University (US)
- Aligarh Muslim University (IN)
- Harvard University (US)
- ORCID
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Published
- 2023
- Pages
- 14676-14688
- OpenAlex
- W4389519798
- Cited by
- 22
- References
- 34
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 3
- FWCI
- 2.35
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- radiology report generation, few-shot prompting, medical report writing, clinical text generation
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