AI-Powered Early Warning Systems For Sepsis: Enhancing Detection Without Alert Fatigue

Abstract

Background: Despite improvements in critical care, sepsis continues to pose a serious threat to global health, contributing to high mortality and long-term morbidity. Positive results depend on early detection and intervention, but traditional screening methods have serious problems with alert fatigue and have low sensitivity and specificity. Clinical decision support (CDS) tools that are integrated with electronic health records (EHRs) have accelerated the detection of sepsis, but frequently at the cost of bombarding physicians with too many low-yield alerts. Objective: This review synthesizes current developments in early warning systems (EWS) for sepsis that are powered by artificial intelligence (AI), looking at how these systems improve detection precision and lessen alert fatigue in clinical settings. Methods: We used PubMed, Scopus, and Web of Science (2015–2025) to do a systematic literature review. The terms we used were "sepsis," "artificial intelligence," "machine learning," "deep learning," and "alert fatigue." To be included, the articles had to be peer-reviewed and talk about AI/ML-based sepsis detection systems that talked about predictive performance, clinical implementation, or ways to deal with alert burden. Results: AI-based EWS, which use machine learning and deep learning, have a higher AUROC (0.85–0.93) than traditional rule-based models and can predict the start of sepsis several hours earlier. Dynamic thresholding, context-aware and tiered alerting, ensemble models, and explainable AI are some of the best ways to cut down on alert fatigue. When integrated into clinical workflows, real-world implementations like TREWS show lower death rates, shorter stays in the ICU, and fewer unnecessary alerts. Conclusion: AI-powered sepsis prediction systems are better at finding cases early and sorting them by risk, and they also help with alert fatigue. For a successful deployment, clinicians must be involved all the time, the design must be centred on workflow, there must be strong data governance, and there must be ethical safeguards to protect trust, usability, and patient safety.