The Role of Artificial Intelligence in the Prediction, Diagnosis, and Management of Sepsis in the Intensive Care Unit (ICU)

Abstract

Background: Sepsis is a leading cause of morbidity and mortality in intensive care units (ICUs) worldwide, contributing to millions of deaths annually. Delayed recognition and treatment remain the most critical factors associated with poor outcomes, as conventional diagnostic approaches such as SIRS, SOFA, and qSOFA often fail to capture the heterogeneity of sepsis. The dynamic physiology of critically ill patients, combined with the complexity of large-scale clinical data, underscores the urgent need for innovative approaches to improve early detection and personalized management. Objective: This review examines the emerging role of Artificial Intelligence (AI) encompassing machine learning, deep learning, natural language processing, and reinforcement learning in predicting, diagnosing, and managing sepsis in ICU settings. Methods: We conducted a comprehensive review of recent AI driven models applied to sepsis, focusing on their ability to predict onset, anticipate organ dysfunction, guide individualized therapy, and optimize antimicrobial stewardship. The analysis also included evaluation of commercially available and FDA-cleared tools, with attention to validation studies, clinical integration, and regulatory considerations. Conclusions: AI has the potential to transform sepsis care in ICUs by enabling earlier diagnosis, supporting clinical decision-making, and personalizing treatment strategies. To realize this promise, future work should focus on enhancing explainability through explainable AI (XAI), conducting large-scale multicenter validation studies, and establishing clear regulatory frameworks. With these advances, AI-driven systems are likely to become integral components of critical care practice.

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