Radiology Report
Radiology reports, crucial for medical diagnosis and treatment, are increasingly being analyzed and generated using artificial intelligence. Current research focuses on improving the accuracy and efficiency of automated report generation and extraction of structured data from unstructured reports, employing large language models (LLMs) like LLAMA and GPT, often augmented with retrieval-augmented generation (RAG) and other techniques like contrastive learning. This work aims to reduce radiologist workload, improve diagnostic accuracy, and facilitate data analysis for research and clinical decision support, with a strong emphasis on ensuring data privacy and clinical validity. The development of new evaluation metrics specifically tailored for radiology reports is also a key area of ongoing investigation.
Papers
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Tianyang Zhong, Wei Zhao, Yutong Zhang, Yi Pan, Peixin Dong, Zuowei Jiang, Xiaoyan Kui, Youlan Shang, Li Yang, Yaonai Wei, Longtao Yang, Hao Chen, Huan Zhao, Yuxiao Liu, Ning Zhu, Yiwei Li, Yisong Wang, Jiaqi Yao, Jiaqi Wang, Ying Zeng, Lei He, Chao Zheng, Zhixue Zhang, Ming Li, Zhengliang Liu, Haixing Dai, Zihao Wu, Lu Zhang, Shu Zhang, Xiaoyan Cai, Xintao Hu, Shijie Zhao, Xi Jiang, Xin Zhang, Xiang Li, Dajiang Zhu, Lei Guo, Dinggang Shen, Junwei Han, Tianming Liu, Jun Liu, Tuo Zhang
Dynamic Multi-Domain Knowledge Networks for Chest X-ray Report Generation
Weihua Liu, Youyuan Xue, Chaochao Lin, Said Boumaraf