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中华重症医学电子杂志 ›› 2025, Vol. 11 ›› Issue (01) : 36 -41. doi: 10.3877/cma.j.issn.2096-1537.2025.01.008

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人工智能爆发增长背景下的重症医学发展
吴骎1, 王波1, 尹万红1, 廖雪莲1,2, 郭军1, 陶攀1, 康焰1,2,()   
  1. 1. 610041 成都,四川大学华西医院重症医学科
    2. 610210 成都,四川大学华西天府医院重症医学科
  • 收稿日期:2025-02-15 出版日期:2025-02-28
  • 通信作者: 康焰

Development of critical care medicine in the context of the explosive growth of artificial intelligence

Qin Wu1, Bo Wang1, Wanhong Yin1, Xuelian Liao1,2, Jun Guo1, Pan Tao1, Ya Kang1,2,()   

  1. 1. Department of Critical Care Medicine,West China Hospital,Sichuan University,Chengdu 610041,China
    2. Department of Critical Care Medicine,West China Tianfu Hospital,Sichuan University,Chengdu 610210,China
  • Received:2025-02-15 Published:2025-02-28
  • Corresponding author: Ya Kang
引用本文:

吴骎, 王波, 尹万红, 廖雪莲, 郭军, 陶攀, 康焰. 人工智能爆发增长背景下的重症医学发展[J/OL]. 中华重症医学电子杂志, 2025, 11(01): 36-41.

Qin Wu, Bo Wang, Wanhong Yin, Xuelian Liao, Jun Guo, Pan Tao, Ya Kang. Development of critical care medicine in the context of the explosive growth of artificial intelligence[J/OL]. Chinese Journal of Critical Care & Intensive Care Medicine(Electronic Edition), 2025, 11(01): 36-41.

人工智能(AI)技术的爆发式发展为重症医学带来了革命性机遇。本研究系统探讨了AI在重症医学信息化中的关键应用场景,包括电子病历智能分析、多模态数据融合、AI辅助临床决策、远程重症监护(Tele-ICU)优化,以及物联网(IoT)、区块链和虚拟现实(VR/AR)等新兴技术的协同作用。本研究通过分析当前AI驱动的重症医学实践,揭示了其在提升诊疗精准性、优化资源配置及降低医疗成本方面的核心价值;同时,研究深入剖析了AI应用面临的挑战,如数据标准化不足、模型可解释性缺陷、隐私安全风险及临床适应性局限,并提出通过联邦学习、可解释人工智能(ⅩAI)技术、政策法规完善及跨学科协作等路径实现突破;进一步展望了未来发展方向,包括AI与终端设备深度整合、全生命周期监护体系构建及精准医疗范式革新。本研究的核心目标在于为重症医学智能化转型提供理论支撑与实践指南,助力AI技术从辅助工具向核心决策引擎的跨越,最终实现重症救治质量与效率的全面提升,推动医疗资源普惠化与患者预后改善。

The explosive growth of artificial intelligence (AI) technology has brought revolutionary opportunities to critical care medicine. This study systematically explores the key applications of AI in critical care informatization,including intelligent electronic medical record (EMR) analysis,multimodal data integration,AI-assisted clinical decision-making,optimization of tele-intensive care unit (Tele-ICU),and the synergistic integration of emerging technologies such as the Internet of Things (IoT),blockchain,and virtual reality/augmented reality (VR/AR). By analyzing current AI-driven practices in critical care,this research highlights its core value in enhancing diagnostic precision,optimizing resource allocation,and reducing healthcare costs. Furthermore,the study delves into the challenges hindering AI adoption,such as insufficient data standardization,model explainability gaps,privacy security risks,and limitations in clinical adaptability. It proposes breakthrough solutions through federated learning,explainable AI (ⅩAI) techniques,policy refinement,and interdisciplinary collaboration. The study also envisions future directions,including deep integration of AI with bedside terminals,construction of a lifecycle monitoring system,and innovation in precision medicine paradigms. The primary objective of this research is to provide theoretical and practical guidance for the intelligent transformation of critical care medicine,facilitating the evolution of AI from an auxiliary tool to a core decision-making engine. Ultimately,this aims to achieve comprehensive improvements in the quality and efficiency of critical care,promote equitable access to medical resources,and enhance patient outcomes.

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