Hadweh Pierre, Niset Alexandre, Salvagno Michele, Al Barajraji Mejdeddine, El Hadwe Salim, Taccone Fabio Silvio, Barrit Sami
Sciense, New York, NY 10027, USA.
Pediatric Intensive Care Unit, Cliniques Universitaires Saint-Luc, 1200 Bruxelles, Belgium.
J Clin Med. 2025 Jun 6;14(12):4026. doi: 10.3390/jcm14124026.
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming clinical decision support systems (CDSSs) in intensive care units (ICUs), where vast amounts of real-time data present both an opportunity and a challenge for timely clinical decision-making. Here, we trace the evolution of machine intelligence in critical care. This technology has been applied across key ICU domains such as early warning systems, sepsis management, mechanical ventilation, and diagnostic support. We highlight a transition from rule-based systems to more sophisticated machine learning approaches, including emerging frontier models. While these tools demonstrate strong potential to improve predictive performance and workflow efficiency, their implementation remains constrained by concerns around transparency, workflow integration, bias, and regulatory challenges. Ensuring the safe, effective, and ethical use of AI in intensive care will depend on validated, human-centered systems supported by transdisciplinary collaboration, technological literacy, prospective evaluation, and continuous monitoring.
人工智能（AI）和机器学习（ML）正在迅速改变重症监护病房（ICU）中的临床决策支持系统（CDSS），在这些病房中，大量实时数据为及时的临床决策既带来了机遇，也带来了挑战。在此，我们追溯重症监护中机器智能的发展历程。这项技术已应用于关键的ICU领域，如早期预警系统、脓毒症管理、机械通气和诊断支持。我们强调了从基于规则的系统向更复杂的机器学习方法的转变，包括新兴的前沿模型。虽然这些工具在提高预测性能和工作流程效率方面显示出强大潜力，但其实施仍受到透明度、工作流程整合、偏差和监管挑战等问题的限制。确保在重症监护中安全、有效地使用人工智能将依赖于经过验证的、以人为本的系统，这些系统得到跨学科合作、技术素养、前瞻性评估和持续监测的支持。
