AI-DRIVEN EARLY WARNING SYSTEMS FOR SEPSIS DETECTION IN ICU SETTINGS: A STRUCTURED REVIEW OF PREDICTIVE PERFORMANCE, CLINICAL INTEGRATION AND EQUITY OUTCOMES

Background: Sepsis remains a primary cause of mortality and organ dysfunction in Intensive Care Units (ICUs) globally. Although AI models show considerable promise for the early identification of sepsis, moving these tools from retrospective performance to bedside utility is hindered by socio-technical barriers, workflow friction, and algorithmic equity issues. Objective: This structured review evaluates the state of AI-driven sepsis Early Warning Systems (EWS), identifying systemic barriers to clinical integration and assessing algorithmic fairness across demographic cohorts. Methodology: A structured review of peer-reviewed literature published in PubMed/MEDLINE and Google Scholar between January 2018 and June 2026 was conducted, focusing on predictive performance, clinical implementation, and algorithmic equity. Results: Retrospective assessments of deep sequential architectures demonstrate high diagnostic accuracy (AUROC>0.95) (Boussina et al., 2024; Siddiqui, 2026). Prospective trials of landmark systems such as TREWS (Adams et al., 2022), COMPOSER (Shashikumar et al., 2025), and CONCERN EWS (Rossetti et al., 2025), confirm clinical utility, yielding mortality reductions of 17% to 35.6%. However, real-world effectiveness is frequently hindered by severe alert fatigue (Wong et al., 2021). Furthermore, because models rely on historical health data, they often inherit systemic biases, risking the exacerbation of existing disparities related to race, gender and socioeconomic status (Chen et al., 2023; Obermeyer et al., 2019; Seyyed-Kalantari et al., 2021). Conclusion: Maximizing the clinical impact of AI-driven EWS requires a paradigm shift from purely performance-centric model development to rigorous, equity-oriented clinical governance guided by international reporting frameworks like TRIPOD-AI (Collins et al., 2024) and DECIDE-AI (Vasey et al., 2022). To operationalize this, we propose the integration of the Clinical AI Safety Officer (CAISO) to maintain real-time algorithmic safety, audit demographic equity, and manage the clinical accountability loop.