Artificial intelligence early-warning systems for sepsis: From risk scores to bedside action
Zekai Yu, Chengwen SongBackground
Sepsis 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.
Objective
This 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.
Methods
We 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.
Results
AI-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.
Conclusion
AI-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.