Artificial intelligence in neonatal sepsis: Scope, challenges, and potential solutions!

Abstract

Neonatal sepsis remains a major cause of neonatal deaths globally. Despite advances, accurate and timely diagnosis is hindered by the limited performance of the current clinical approaches, imperfect laboratory biomarkers, and long turnaround time of blood cultures. Artificial intelligence (AI), with its ability to identify patterns and learn continuously (machine learning), seems promising. Basic steps in model development include data filtration, train: test split, feature selection, choosing appropriate algorithms, and evaluating performance using a reference standard. In neonatal sepsis, the role of AI spans from predicting sepsis and related outcomes to formulating an individualized treatment approach for the neonate. Existing models, largely from high-income countries, report encouraging diagnostic accuracy but face methodological limitations, lack external validation, and remain somewhat distant from bedside application. Additional barriers to their generalizability include lack of uniform definition of sepsis, variations in disease and pathogen profiles in different settings (particularly in developing countries), availability of electronic health data, tweaks in feature selection, and ethical and legal challenges. This review synthesizes current evidence, highlights gaps, and outlines priorities for future research. We call for a collaborative effort from AI and neonatal experts to devise robust, context-specific solutions.