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：本文介绍Radiology-GPT，这是一个专为放射学领域设计的大型语言模型。通过在海量放射学领域知识数据集上采用指令微调方法，Radiology-GPT在性能上显著优于StableLM、Dolly和LLaMA等通用语言模型。该模型在放射诊断、科研及医患沟通方面展现出卓越的多功能性，为临床自然语言处理的未来发展提供了重要推动力。Radiology-GPT的成功实现表明，在严格遵循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的本地化部署具有巨大潜力。开发能够满足不同医院个性化需求的大规模语言模型这一前景，为医疗人工智能的发展指明了方向。这类模型将对话能力与领域专业知识相融合，必将推动未来医疗AI领域的创新发展。Radiology-GPT的演示版本可通过以下链接获取：https://huggingface.co/spaces/allen-eric/radiology-gpt。