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在重症监护中的大型语言模型

Laurens A Biesheuvel¹, Jessica D Workum^2,3, Merijn Reuland¹

¹Department of Intensive Care Medicine, Center for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam Institute for Immunity and Infectious Diseases, Amsterdam Cardiovascular Science, Amsterdam UMC, Vrije Universiteit, University of Amsterdam, Amsterdam, The Netherlands.

Journal of intensive medicine

|April 17, 2025

概括

大型语言模型 (LLM) 通过增强数据分析和临床支持,为重症监护医学提供了变革性的潜力. 负责任的实施和临床医生培训对于安全有效地利用这些AI进步至关重要.

科学领域:

人工智能在医学中的应用
自然语言处理应用程序
临床护理信息学临床护理信息学

背景情况:

大型语言模型 (LLM) 和聊天生成预训练变换器 (ChatGPT) 代表了自然语言处理 (NLP) 的重大进步.
重症护理医学产生了大量的非结构化数据,为AI带来了独特的挑战和机会.
法学士在理解和生成类似人类文本方面的能力与临床环境高度相关.

研究的目的:

探索LLMs在重症监护医学中的潜在应用.
确定与将LLM整合到重症监护机构相关的好处和挑战.
讨论混合人工智能的作用和负责任的实施策略.

主要方法:

审查目前在NLP的LLM能力.
在重症监护中分析潜在的用例:行政支持,临床决策支持,患者沟通和数据质量改善.
讨论包括人工智能幻觉,伦理问题和人工智能识字需求在内的挑战.
探索混合人工智能方法,将LLMs与传统机器学习相结合.

主要成果:

通过LLM,可以实现文档自动化,总结患者病历,帮助诊断和个性化沟通.
关键的挑战包括不准确信息 ("幻觉") 的风险,道德困境,以及临床医生AI素养的必要性.
混合人工智能模型可以利用LLM的优势,同时减轻弱点.

结论:

法律法学有潜力显著改变重症监护实践.
谨慎的整合,监管合规和持续的验证对于安全有效的使用至关重要.
优先考虑负责任的部署和全面的临床医生培训对于最大限度地提高效益和确保患者安全至关重要.

关键词:

人工智能的人工智能是人工智能.关键护理医学关键护理医学重症监护医药重症监护医药重症监护医药重症监护医药大型语言模型.机器学习是机器学习.自然语言处理自然语言处理.

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