arXiv 2407.192562024-07-27cs.AIcs.CLcs.LG

随机鹦鹉还是ICU专家？大语言模型在重症医学中的应用：一项范围综述

Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review

National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Advanced Institute of Information Technology, Peking University（北京大学信息技术高等研究院）；Peking University Third Hospital（北京大学第三医院）；University of Liverpool（利物浦大学）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）；University of California, Los Angeles（加州大学洛杉矶分校）；Peking University People’s Hospital（北京大学人民医院）

Tongyue Shi, Jun Ma, Zihan Yu, Haowei Xu, Minqi Xiong, Meirong Xiao, Yilin Li, Huiying Zhao, Guilan Kong

备注：: 28 pages, 5 figures

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AI总结：

本范围综述全面回顾了24篇文献，将大语言模型在重症医学中的应用归纳为临床决策支持、医疗文档及医学教育三类，分析了其优势与幻觉、隐私等挑战，为该领域首份综述。

摘要：

随着人工智能（AI）的快速发展，大语言模型（LLMs）在自然语言理解、推理和生成方面展现出了强大的能力，吸引了大量将LLMs应用于健康和医学领域的研究兴趣。重症医学（CCM）为危重患者提供诊断和治疗，这些患者通常需要在重症监护病房（ICUs）中进行密集监测和干预。LLMs能否应用于CCM？在辅助临床决策方面，LLMs究竟是像随机鹦鹉还是ICU专家？本范围综述旨在全面描绘LLMs在CCM中的应用全景。检索了七个数据库（包括PubMed、Embase、Scopus、Web of Science、CINAHL、IEEE Xplore和ACM Digital Library）中2019年1月1日至2024年6月10日的文献。纳入了讨论LLMs在重症监护环境中应用的同行评审期刊和会议文章。从最初的619篇文章中，最终选定了24篇进行最终综述。本综述将LLMs在CCM中的应用分为三类：临床决策支持、医疗文档和报告，以及医学教育和医患沟通。LLMs在处理非结构化数据方面具有优势，且不需要手动特征工程。同时，将LLMs应用于CCM面临诸多挑战，包括幻觉、可解释性差、偏见与对齐挑战，以及隐私和伦理问题。未来研究应增强模型的可靠性和可解释性，整合最新的医学知识，并加强隐私和伦理准则。随着LLMs的发展，它们可能成为CCM中的关键工具，帮助改善患者预后并优化医疗服务。本研究是首次对LLMs在CCM中的应用进行综述，旨在帮助研究人员、临床医生和政策制定者了解LLMs在CCM中的现状和未来潜力。

英文摘要：

With the rapid development of artificial intelligence (AI), large language models (LLMs) have shown strong capabilities in natural language understanding, reasoning, and generation, attracting amounts of research interest in applying LLMs to health and medicine. Critical care medicine (CCM) provides diagnosis and treatment for critically ill patients who often require intensive monitoring and interventions in intensive care units (ICUs). Can LLMs be applied to CCM? Are LLMs just like stochastic parrots or ICU experts in assisting clinical decision-making? This scoping review aims to provide a panoramic portrait of the application of LLMs in CCM. Literature in seven databases, including PubMed, Embase, Scopus, Web of Science, CINAHL, IEEE Xplore, and ACM Digital Library, were searched from January 1, 2019, to June 10, 2024. Peer-reviewed journal and conference articles that discussed the application of LLMs in critical care settings were included. From an initial 619 articles, 24 were selected for final review. This review grouped applications of LLMs in CCM into three categories: clinical decision support, medical documentation and reporting, and medical education and doctor-patient communication. LLMs have advantages in handling unstructured data and do not require manual feature engineering. Meanwhile, applying LLMs to CCM faces challenges, including hallucinations, poor interpretability, bias and alignment challenges, and privacy and ethics issues. Future research should enhance model reliability and interpretability, integrate up-to-date medical knowledge, and strengthen privacy and ethical guidelines. As LLMs evolve, they could become key tools in CCM to help improve patient outcomes and optimize healthcare delivery. This study is the first review of LLMs in CCM, aiding researchers, clinicians, and policymakers to understand the current status and future potentials of LLMs in CCM.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。