Summarizing Radiology Reports Findings into Impressions
Quod Analytics（Quod分析公司）；University of Texas Austin（德克萨斯大学奥斯汀分校）
Raul Salles de Padua, Imran Qureshi
备注：
This version reverts to the original preprint, following the advice from the Artificial Intelligence in Health editorial office. The published version is peer-reviewed and available in the journal (see external DOI). The preprint remains unchanged to maintain version transparency, as noted in the further disclosure section of the published article
Patient hand-off and triage are two fundamental problems in health care. Often doctors must painstakingly summarize complex findings to efficiently communicate with specialists and quickly make decisions on which patients have the most urgent cases. In pursuit of these challenges, we present (1) a model with state-of-art radiology report summarization performance using (2) a novel method for augmenting medical data, and (3) an analysis of the model limitations and radiology knowledge gain. We also provide a data processing pipeline for future models developed on the the MIMIC CXR dataset. Our best performing model was a fine-tuned BERT-to-BERT encoder-decoder with 58.75/100 ROUGE-L F1, which outperformed specialized checkpoints with more sophisticated attention mechanisms. We investigate these aspects in this work.
Word Graph Guided Summarization for Radiology Findings
National University of Defense Technology（国防科技大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳市大数据研究院）
An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT
School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；School of Life Science and Technology, University of Electronic Science and Technology of China（电子科技大学生命科学与技术学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；School of Computing, University of Georgia（佐治亚大学计算学院）；Department of Computer Science and Engineering, The University of Texas at Arlington（德克萨斯大学阿灵顿分校计算机科学与工程系）；Department of Radiology, Massachusetts General Hospital（麻省总医院放射科）
Multilingual Natural Language Processing Model for Radiology Reports -- The Summary is all you need!
ALGORITMI Research Centre/LASI, University of Minho（米尼奥大学 ALGORITMI 研究中心/LASI）；Institute for Artificial Intelligence in Medicine, University Medicine Essen (AöR)（埃森大学医学院人工智能医学研究所）；Department of Diagnostic and Interventional Radiology and Neuroradiology, University Medicine Essen (AöR)（埃森大学医学院诊断与介入放射学及神经放射学系）；Institute for Computer Graphics and Vision, Graz University of Technology（格拉茨技术大学计算机图形与视觉研究所）
A Dual-View Approach to Classifying Radiology Reports by Co-Training
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所（Amii））；School of Public Health, University of Alberta（阿尔伯塔大学公共卫生学院）；Amii（Amii（阿尔伯塔机器智能研究所））
MGH Radiology Llama: A Llama 3 70B Model for Radiology
School of Computing, University of Georgia（佐治亚大学计算学院）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院及哈佛医学院放射科）
Paying Per-label Attention for Multi-label Extraction from Radiology Reports
Canon Medical Research Europe（佳能医疗欧洲研究中心）；University of St Andrews（圣安德鲁斯大学）；Institute of Neuroscience & Psychology, University of Glasgow（格拉斯哥大学神经科学与心理学研究所）；University of Edinburgh（爱丁堡大学）
Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang Ming Chiao Tung University（国立阳明交通大学）；City University of Hong Kong（香港城市大学）
