医学中的大型语言模型
Arun James Thirunavukarasu1,2, Darren Shu Jeng Ting3,4,5, Kabilan Elangovan6
1University of Cambridge School of Clinical Medicine, Cambridge, UK.
Nature medicine
|July 17, 2023
概括
大型语言模型 (LLM) 为医疗保健提供了令人兴奋的潜力,但它们的应用需要仔细考虑. 本综述探讨了临床环境中LLM聊天机器人的能力和挑战,以指导其负责任的采用.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 大型语言模型 (LLM) 证明了对非提示性查询响应的能力,引发了对其医疗整合的兴趣和担忧.
- 像ChatGPT一样,生成人工智能 (AI) 聊天机器人是先进的LLM应用程序,越来越与医学领域相关.
研究的目的:
- 提供对LLM开发和在临床环境中的应用的基本理解.
- 批判性地评估LLMs在医学实践,教育和研究中的优势,局限性和潜在影响.
- 作为临床医生导航AI在医疗保健中的不断发展的景观的入门书.
主要方法:
- 审查LLM开发流程,重点关注生成性AI聊天机器人,如ChatGPT.
- 分析目前在生物医学环境中LLM聊天机器人的部署和应用.
- 讨论将LLM整合到医疗保健工作流程中的潜在好处和缺点.
主要成果:
- 包括ChatGPT在内的LLM应用程序正在积极探索和部署在各种生物医学领域.
- 在医疗保健中部署LLM聊天机器人的初步结果令人鼓舞,但结果不一,强调需要进一步评估.
- 在临床,教育和研究活动中,LLM具有提高效率和有效性的潜力.
结论:
- 法学士技术为提高医疗效率和有效性提供了重大机会.
- 仔细考虑LLM的优势和局限性对于成功融入医疗保健至关重要.
- 临床医生在确定在患者护理和医疗实践中适当和有益地使用LLM技术方面发挥着关键作用.
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