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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.

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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.