Pivotal challenges in artificial intelligence and machine learning applications for neonatal care

Abstract

Clinical decision support systems (CDSS) that are developed based on artificial intelligence and machine learning (AI/ML) approaches carry transformative potentials in improving the way neonatal care is practiced. From the use of the data available from electronic health records to physiological sensors and imaging modalities, CDSS can be used to predict clinical outcomes (such as mortality rate, hospital length of state, or surgical outcome) or early warning signs of diseases in neonates. However, only a limited number of clinical decision support systems for neonatal care are currently deployed in healthcare facilities or even implemented during pilot trials (or prospective studies). This is mostly due to the unresolved challenges in developing a real-time supported clinical decision support system, which mainly consists of three phases: model development, model evaluation, and real-time deployment. In this review, we introduce some of the pivotal challenges and factors we must consider during the implementation of real-time supported CDSS.

Keywords: Artificial intelligence; Clinical decision support; Clinical decision support models; Critical care; Machine learning; Neonatology.