Artificial Intelligence for Early Prediction and Diagnosis of Neonatal Sepsis: Current Evidence, Challenges, and Future Directions

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Abstract
Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while timely diagnosis continues to be challenging because of nonspecific clinical manifestations and limitations of conventional diagnostic methods. Recent advances in artificial intelligence (AI) have created new opportunities for the early prediction and diagnosis of neonatal sepsis through the analysis of large and complex clinical datasets. This structured narrative review summarizes current evidence regarding AI-based approaches for neonatal sepsis prediction and diagnosis. A literature search of PubMed, Scopus, and Google Scholar identified studies evaluating machine learning, deep learning, and advanced predictive analytics using clinical, laboratory, physiological, electronic health record, and multi-omics data. Current evidence demonstrates that AI models, particularly ensemble learning, gradient boosting, and deep learning approaches, can achieve strong predictive performance and identify infants at increased risk of sepsis hours before conventional clinical recognition. Continuous physiological monitoring and multimodal data integration appear particularly promising for real-time prediction. However, important challenges remain, including limited external validation, small and heterogeneous datasets, concerns regarding interpretability, and unresolved ethical and regulatory issues. Future progress will depend on multicenter collaboration, explainable AI frameworks, federated learning, and multimodal predictive models. Artificial intelligence has the potential to become a valuable decision-support tool that enhances early sepsis recognition and supports precision neonatal care.

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