Workum J D, Meyfroidt G, Bakker J, Jung C, Tobin J M, Gommers D, Elbers P W G, van der Hoeven J G, Van Genderen M E
Department of Adult Intensive Care, Erasmus University Medical Center, Rotterdam, the Netherlands; Erasmus MC Datahub, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Intensive Care, Elisabeth-TweeSteden Hospital, Tilburg, the Netherlands.
Department and Laboratory of Intensive Care Medicine, University Hospitals Leuven and KU, Leuven, Belgium.
J Crit Care. 2026 Feb;91:155262. doi: 10.1016/j.jcrc.2025.155262. Epub 2025 Sep 23.
Artificial intelligence (AI) has the potential to revolutionize critical care medicine by enhancing patient care, improving resource allocation and reducing clinician workload. Despite this promise, many AI applications remain confined to scientific research rather than being integrated into everyday clinical practice. This manuscript aims to help intensivists prepare themselves and their intensive care units (ICUs) for AI implementation. It provides a comprehensive yet practical roadmap, detailing AI methods, applications, responsible AI principles, common roadblocks and implementation strategies. We propose a three-tiered risk-based approach to AI implementation, starting with low-risk low-complexity administrative AI, progressing to logistical AI, and finally integrating medical AI as clinical decision support systems. This ensures a gradual build-up of AI skills, technical AI readiness of the ICU, incremental value demonstration and alignment with evolving regulatory standards. For each AI project, responsible AI principles should be incorporated and adequately addressed throughout the entire AI lifecycle, from development to validation to implementation and scaling. Common roadblocks for AI implementation including technical issues (such as data quality and interoperability issues), organizational challenges (such as lack of a clear vision and strategy), and clinical concerns (such as limited AI literacy among staff), should be addressed proactively. By following this roadmap, ICUs can achieve sustainable AI integration, ultimately improving patient outcomes and clinician experience. The future of critical care lies in the responsible and strategic adoption of AI, with intensivists playing a central role in shaping its implementation.
人工智能（AI）有潜力通过改善患者护理、优化资源分配和减轻临床医生工作量，给重症监护医学带来变革。尽管有此前景，但许多人工智能应用仍局限于科学研究，而非融入日常临床实践。本文旨在帮助重症监护医生为人工智能的实施做好自身及重症监护病房（ICU）的准备。它提供了一份全面而实用的路线图，详细介绍了人工智能方法、应用、负责任的人工智能原则、常见障碍及实施策略。我们提出一种基于风险的三层人工智能实施方法，从低风险、低复杂性的管理型人工智能开始，发展到后勤型人工智能，最后将医疗人工智能作为临床决策支持系统加以整合。这确保了人工智能技能的逐步积累、ICU对人工智能技术的准备就绪、价值的逐步显现以及与不断演变的监管标准保持一致。对于每个人工智能项目，在从开发到验证再到实施和扩展的整个人工智能生命周期中，都应纳入并充分解决负责任的人工智能原则。人工智能实施的常见障碍包括技术问题（如数据质量和互操作性问题）、组织挑战（如缺乏明确的愿景和战略）以及临床问题（如工作人员的人工智能知识有限），应积极加以解决。通过遵循此路线图，ICU能够实现人工智能的可持续整合，最终改善患者预后和临床医生的体验。重症监护的未来在于负责任且战略性地采用人工智能，重症监护医生在塑造其实施过程中发挥核心作用。
