The Translational Gap in Neonatal Sepsis Prediction: A Scoping Review of Prospective Artificial Intelligence Validations in the NICU

Abstract

Background and Clinical Context Neonatal sepsis remains one of the leading causes of morbidity and mortality among premature infants in the Neonatal Intensive Care Unit (NICU). Because the clinical signs of sepsis in premature infants are often subtle and non-specific, diagnosis is frequently delayed, leading to rapid deterioration. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as highly promising tools to predict sepsis hours or even days before clinical onset by analyzing continuous physiological data (such as heart rate variability) and electronic health records. However, a critical "translational gap" exists. The current scientific literature is overwhelmed with retrospective, in silico models trained on historical databases (like MIMIC-III) that have never been tested on real patients. Despite the technological hype, very few algorithms have successfully transitioned from the computer science laboratory to bedside implementation in the NICU. Purpose and Objectives The primary purpose of this scoping review is to critically map the current landscape of AI and ML models that have achieved prospective, real-time clinical validation for predicting neonatal sepsis in the NICU. Rather than evaluating the mathematical accuracy of theoretical models, this research aims to investigate the practical realities of bedside AI deployment. The core objectives are to: Isolate the pioneering studies that have successfully connected predictive algorithms to patient monitors and clinical workflows in real-time. Identify and categorize the translational barriers preventing widespread clinical adoption of these technologies. Evaluate the clinical utility of the predictive alerts (lead time) generated by these models prior to the standard clinical suspicion of sepsis. Methodology The project follows a rigorous scoping review methodology, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) framework. A highly specific search strategy is applied across medical and biomedical engineering databases, including PubMed (MEDLINE), Embase, and IEEE Xplore. The study utilizes a strict "bedside filter" for eligibility: all retrospective models are systematically excluded. Data extraction focuses on algorithm specifications, continuous vital sign inputs, predictive lead time, and operational implementation challenges reported by the clinical teams. Expected Outcomes and Clinical Impact This research is expected to expose the profound disparity between AI development and real-world clinical application in neonatology. By synthesizing data from prospective validations, the expected outcomes include: Identification of Key Implementation Barriers: A structured analysis of the practical challenges of AI in the NICU, including hardware/software interoperability issues, high rates of false positives leading to "alarm fatigue" among nursing staff, and the "black box" phenomenon that hinders physician trust. A Paradigm Shift in Research Focus: The findings will provide a strong evidence base to argue that future research funding and efforts must shift away from redundant retrospective data mining and toward prospective clinical trials. Blueprint for Future AI Deployment: By understanding what went wrong—or right—in the few models that reached the bedside, this project will offer actionable insights for healthcare policymakers, biomedical engineers, and neonatologists to design better, workflow-integrated digital health solutions. Ultimately, this project aims to advance the conversation around precision medicine and digital care, ensuring that AI serves as a functional, life-saving tool for vulnerable premature infants rather than just a theoretical engineering concept.