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 , Xiang Li
{"title":"放射学- gpt:用于放射学的大型语言模型","authors":"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 , Xiang Li","doi":"10.1016/j.metrad.2025.100153","DOIUrl":null,"url":null,"abstract":"<div><div>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 <span><span>https://huggingface.co/spaces/allen-eric/radiology-gpt</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"3 2","pages":"Article 100153"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Radiology-GPT: A large language model for radiology\",\"authors\":\"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 , Xiang Li\",\"doi\":\"10.1016/j.metrad.2025.100153\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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 <span><span>https://huggingface.co/spaces/allen-eric/radiology-gpt</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":100921,\"journal\":{\"name\":\"Meta-Radiology\",\"volume\":\"3 2\",\"pages\":\"Article 100153\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Meta-Radiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2950162825000219\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meta-Radiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950162825000219","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Radiology-GPT: A large language model for radiology
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.