Artificial intelligence early-warning systems for sepsis: From risk scores to bedside action

BackgroundSepsis remains a leading cause of preventable mortality in critical care. Timely recognition is essential for effective bundle-based management, and artificial intelligence-enabled early-warning systems (AI-EWS) may facilitate earlier detection by converting electronic health record and bedside monitoring data into actionable risk estimates.ObjectiveThis review aimed to synthesize recent advances in the development, validation, and real-world deployment of AI-EWS for sepsis and to identify the factors that determine their clinical benefit at the unit level.MethodsWe conducted a structured narrative review based on a targeted search of PubMed, IEEE Xplore, and Google Scholar from database inception to December 2025. Search terms combined artificial intelligence, machine learning, or deep learning with sepsis and early-warning score or prediction. Priority was given to studies addressing prospective performance, clinical implementation, alert fatigue, workflow integration, governance, and regulation.ResultsAI-EWS may identify sepsis risk earlier than traditional scoring systems; however, retrospective discrimination alone does not ensure clinical benefit. Effective deployment depends on prospective calibration, site-specific threshold setting, precision-recall trade-offs, manageable alert budgets, and integration with nurse-centered workflows, standardized order sets, escalation pathways, and feedback loops. Major implementation risks include alert fatigue, data drift, and limited transportability. Silent-mode trials, group-aware recalibration, regular calibration and drift audits, throttle and rollback rules, and auditable change logs can help mitigate these risks.ConclusionAI-EWS should be evaluated as clinical interventions rather than solely as predictive models. Calibrated risk communication, unit-specific operating points, human-centered workflow integration, and accountable governance are essential for achieving safe, equitable, and measurable improvements in sepsis care.