近年来，大语言模型（LLMs）技术的突破为重症医学这一强调多学科协作、实时监测和及时干预的学科提供了新的技术范式。通过整合多模态数据、实时综合分析，LLMs在病情监测、辅助诊疗与临床效率提升中展现出独特潜力。然而，重症医学对个体化诊疗思维、患者动态数据解析及循证的严苛要求，对通用模型的专科能力提出了重大挑战。本文结合技术探索和临床实践，系统探讨LLM在重症医学中的应用路径，并从融入核心诊疗场景、技术迭代、标准化建设以及医工协同四个方面提出未来发展展望。

Large language models (LLMs) are emerging as a transformative tool in critical care medicine, a discipline that fundamentally relies on multidisciplinary collaboration, real-time monitoring, and timely intervention. With their capability to integrate and synthesize multi-modal data (e.g., clinical notes, physiological signals), LLMs demonstrate significant potential to enhance condition monitoring, provide clinical decision support, and improve overall workflow efficiency. However, the rigorous demands of critical care -including the needs for individualized clinical reasoning, accurate interpretation of dynamic high-stakes patient data, and strict adherence to evidence - based practice-present substantial challenges for the direct application of general - purpose LLMs. This review systematically explores the potential application pathways for LLMs in critical care medicine by integrating insights from technical explorations and clinical practice. Furthermore, we propose a future roadmap focusing on four key areas: deep integration into core clinical workflows, continuous technological refinement, establishment of robust standards and validation frameworks, and fostering effective collaboration between clinicians and engineers.