Radiology-GPT: A large language model for radiology

作者：Zhengliang Liu, Yiwei Li, Peng Shu, Aoxiao Zhong, Hanqi Jiang, Yi Pan, Longtao Yang, Chao Ju, Zihao Wu, Chong Ma, Cheng Chen, Sekeun Kim, Haixing Dai, Lin Zhao, Lichao Sun, Dajiang Zhu, Jun Liu, Wei Liu, Dinggang Shen, Quanzheng Li, Tianming Liu, Xiang Li · 发表于：Meta-Radiology · 年份：2025 · DOI：10.1016/j.metrad.2025.100153 · 被引用次数：29 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Radiology practices and education、Artificial Intelligence in Healthcare and Education

We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general language models such as StableLM, Dolly, and LLaMA. It exhibits significant versatility in radiological diagnosis , research, and communication. This work serves as a catalyst for future developments in clinical NLP. The successful implementation of Radiology-GPT is indicative of the potential of localizing generative large language models, specifically tailored for distinctive medical specialties, while ensuring adherence to privacy standards such as HIPAA. The prospect of developing individualized, large-scale language models that cater to specific needs of various hospitals presents a promising direction. The fusion of conversational competence and domain-specific knowledge in these models is set to foster future development in healthcare AI. A demo of Radiology-GPT is available at https://huggingface.co/spaces/allen-eric/radiology-gpt .