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20202023
most citedRadGraph: Extracting Clinical Entities and Relations from Radiology Reports

68 citations · 146 across the 5 of their papers we have counts for

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cs.CL2023

Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization

Dave Van Veen, Cara Van Uden, Louis Blankemeier +16

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large langua…

cs.CL2023

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

Dave Van Veen, Cara Van Uden, Maayane Attias +9

We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain ad…

cs.CL20224 cited

Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards

Jean-Benoit Delbrouck, Pierre Chambon, Christian Bluethgen +3

Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying po…

cs.CL202168 cited

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Saahil Jain, Ashwin Agrawal, Adriel Saporta +9

Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In…

cs.CL202016 cited

Biomedical and Clinical English Model Packages in the Stanza Python NLP Library

Yuhao Zhang, Yuhui Zhang, Peng Qi +2

We introduce biomedical and clinical English model packages for the Stanza Python NLP library. These packages offer accurate syntactic analysis and named entity recognition capabil…