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.

Background: While laparoscopic surgical procedures have various advantages over traditional open techniques, artificial pneumoperitoneum is associated with severe bradycardia and cardiac arrest. Dexmedetomidine, an imidazole derivative that selectively binds to α2-receptors and has sedative and analgesic properties, can cause hypotension and bradycardia. Our primary aim was to assess the association between dexmedetomidine use and intraoperative bradycardia during laparoscopic cholecystectomy. Methods: We performed a systematic review with a meta-analysis and trial sequential analysis using the following PICOS: adult patients undergoing endotracheal intubation for laparoscopic cholecystectomy (P); intravenous dexmedetomidine before tracheal intubation (I); no intervention or placebo administration (C); intraoperative bradycardia (primary outcome), intraoperative hypotension, hemodynamics at intubation (systolic blood pressure, mean arterial pressure, heart rate), dose needed for induction of anesthesia, total anesthesia requirements (both hypnotics and opioids) throughout the procedure, and percentage of patients requiring postoperative analgesics and experiencing postoperative nausea and vomiting and/or shivering (O); randomized controlled trials (S). Results: Fifteen studies were included in the meta-analysis (980 patients). Compared to patients that did not receive dexmedetomidine, those who did had a higher risk of developing intraoperative bradycardia (RR: 2.81, 95% CI [1.34, 5.91]) and hypotension (1.66 [0.92,2.98]); however, they required a lower dose of intraoperative anesthetics and had a lower incidence of postoperative nausea and vomiting. In the trial sequential analysis for bradycardia, the cumulative z-score crossed the monitoring boundary for harm at the tenth trial. Conclusions Patients undergoing laparoscopic cholecystectomy who receive dexmedetomidine during tracheal intubation are more likely to develop intraoperative bradycardia and hypotension.