放射学- gpt:用于放射学的大型语言模型

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
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引用次数: 0

摘要

我们介绍了放射学- gpt,一个大型放射学语言模型。在放射学领域知识的广泛数据集上使用指令调优方法,与一般语言模型(如StableLM, Dolly和LLaMA)相比,radiology - gpt表现出优越的性能。它在放射学诊断、研究和交流方面表现出显著的多功能性。这项工作为临床NLP的未来发展提供了催化剂。放射学- gpt的成功实施表明,在确保遵守HIPAA等隐私标准的同时,专门为独特的医学专业量身定制的生成大型语言模型具有本地化的潜力。开发个性化的、大规模的语言模型,以满足各种医院的特定需求,是一个很有前景的方向。在这些模型中,会话能力和特定领域知识的融合将促进医疗保健人工智能的未来发展。放射学-gpt的演示可在https://huggingface.co/spaces/allen-eric/radiology-gpt上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Radiology-GPT: A large language model for radiology

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.
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