Integrating Artificial Intelligence in Neonatal Care: Clinical Uses and Socioeconomic Factors

Background: Artificial intelligence (AI) is gradually transforming neonatal and pediatric intensive care units (NICUs and PICUs) by enhancing diagnostic accuracy, risk evaluation, and clinical decision support. However, integrating AI into these vital care settings faces challenges related to data limitations, clinician acceptance, and socioeconomic disparities. Objective: This review examines the clinical potential of AI especially machine learning (ML) and deep learning (DL) in NICUs and PICUs, while evaluating the socioeconomic factors that influence AI deployment, effectiveness, and equity. Methods: A comprehensive literature review was conducted, focusing on applications of AI in early diagnosis, patient surveillance, imaging assessment, and transport logistics in neonatal and pediatric ICUs. Factors related to socioeconomic status affecting AI deployment, such as provider demographics, healthcare systems, and geographic inequalities, were examined. Findings: AI models show better early identification of urgent conditions like sepsis and respiratory distress, streamline clinical processes, and improve resource management. Nevertheless, differences in access to AI and its performance are present, especially in low-resource environments because of inadequate infrastructure, biased data, and differing levels of clinician preparedness. Approaches like federated learning and explainable AI could address certain challenges