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.

翻译：我们提出放射学-GPT，这是一个专为放射学领域设计的大型语言模型。通过在包含大量放射学领域知识的指令调优数据集上进行训练，放射学-GPT在放射学诊断、研究和沟通中展现出显著的多功能性，其性能优于StableLM、Dolly和LLaMA等通用语言模型。本工作为临床自然语言处理（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等隐私标准的前提下，针对特定医学专科进行生成式大型语言模型本地化具有巨大潜力。开发适应不同医院具体需求的个性化大规模语言模型的前景令人期待。此类模型将对话能力与领域专业知识相融合，有望推动医疗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获取。