Artificial intelligence in medication management and drug dosing in critical care

Background: Adequate medication dosing in the intensive care unit (ICU) remains challenging due to rapid physiological variability, narrow therapeutic windows, and evolving standards of care. Artificial intelligence (AI) offers promise for personalized dosing, but its accuracy and clinical utility remain uncertain. Methods: We conducted a systematic scoping review (2011-2025) of PubMed, Embase, and Cochrane, following the Joanna Briggs Institute (JBI) and preferred reporting Items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines (open science framework [OSF]-registered). Eligible studies applied AI methods ( e.g. , machine learning [ML], deep learning [DL], reinforcement learning [RL], and natural language processing [NLP]) to ICU medication dosing or monitoring. Results: Data extraction included study design, drug class, model inputs and outputs, algorithms, validation approach, and reported outcomes. Forty-seven studies met the inclusion criteria, which were mostly retrospective and focused on adult ICU populations over the past five years. Antibiotics and anticoagulants were the most studied classes, with vancomycin being the most common. Models primarily used labs, vitals, and demographics, with limited incorporation of time series, pharmacokinetic data, or clinical notes. Outputs spanned dose prediction, serum concentration, risk stratification, adverse events, and decision support. Algorithms evolved from classical ML and ensembles to RL and long short-term memory (LSTM)-based models, although a few advanced beyond retrospective validation. Discussion: AI systems showed strong predictive performance and some clinical benefits ( e.g. , heparin target attainment, sedative reduction, and adverse event detection), but the field remains fragmented and clinically immature. Newer architectures and systematic explainability are lacking, and generalizability is uncertain. AI-enabled dosing holds promise for precision pharmacotherapy in critical illness but requires prospective multicenter validation, inclusion of population pharmacokinetic (PK) parameters, diverse cohorts, explainable design, and clinical integration.