Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice

Background: Critically ill patients generate large volumes of complex data, creating challenges for timely clinical decision making in intensive care units (ICUs). Artificial intelligence (AI) has emerged as a promising tool for supporting diagnosis, monitoring, prognostication, and workflow optimization in this setting. This scoping review aimed to map current AI applications in critical care and identify practical clinical applications. Methods: A systematic search of the MEDLINE, Scopus, and EMBASE databases was conducted for studies published between January 2015 and June 2025. Eligible studies evaluated practical AI applications in ICU settings involving patients, relatives, or healthcare professionals. Data pertaining to study design, AI techniques, clinical domains, outcomes, model characteristics, and implementation features were extracted. Results: In total, 112 studies were included. Most were retrospective observational studies (59.8%) focusing on adult populations. Machine learning was the predominant technology used (76.8%), and the main clinical applications were outcome and mortality predictions, early warning systems, and monitoring, particularly in neurological and respiratory domains. Notably, 24.1% of included studies relied on North American public databases, raising concerns about geographic data monoculture, and only 27.7% of the systems provided real-time bedside applications. Most systems remained at the experimental stage, with limited real-world implementation, heterogeneous performance reporting, and a frequent lack of external validation. Conclusions: AI applications in ICUs have expanded rapidly and show substantial promise for improving patient care and workflow efficiency. Future research should prioritize prospective multicenter validation, explainability, and implementation science to ensure the safe and effective integration of AI into critical care.