Artificial Intelligence in Intensive Care: Clinical Decision Support, Physician Oversight, and the Limits of Algorithmic Predic

Artificial intelligence is increasingly relevant to intensive care because the intensive care unit generates dense, longitudinal data while requiring rapid decisions under uncertainty. Contemporary systems can detect physiological patterns, estimate risk, and support recognition of deterioration, sepsis, ventilatory problems, and other clinically important events. Their growing predictive capability, however, creates a conceptual problem: a probability is not a clinical decision, and improved model performance does not necessarily produce better patient outcomes. This article examines artificial intelligence in intensive care from the perspective of clinical decision-making rather than technical performance alone. It distinguishes rule-based clinical decision support from data-driven prediction, analyzes the transition from prediction to action, and considers early-warning systems, sepsis, mechanical ventilation, explainability, automation bias, human oversight, generalizability, and responsibility. Recent randomized evidence suggests that AI and computerized decision support can improve selected process measures in adult intensive care, while consistent patient-centered benefit remains limited. The article argues that artificial intelligence should be treated as an additional source of clinical information whose principal value lies in reducing informational uncertainty and directing attention to clinically important patterns. A model of human-AI decision-making is proposed in which algorithmic assessment is followed by physician validation, contextual adjustment, clinical action, reassessment, and feedback. Within this framework, AI may strengthen clinical judgment, but predictive capability alone does not justify greater decision authority. Safe implementation therefore requires external and local validation, meaningful physician oversight, calibrated reliance, institutional governance, and continuing post-deployment evaluation.