Artificial Intelligence Applications in Neonatal Critical Care: A Scoping Review

Abstract

The development of artificial intelligence (AI) approaches impacted drug discovery, medical imaging, customized diagnostics, and therapeutics. Medicine will be transformed by AI. One such area of medicine where AI is significantly improving care is neonatology. The objective of this scoping review is to explore the applications of AI in neonatal critical care and its outcome. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a scoping review was conducted utilizing the Web of Science, MEDLINE (PubMed), and Scopus databases. The search was limited to full-text publications on AI applications in neonatal critical care that were published between January 1, 2019, and December 31, 2023. Articles specifically addressing the application of AI in neonatal care have been considered within the scope of this review. At least three reviewers had independently executed the screening, data abstraction, and exploration. Database searches yielded 631 articles, of which 11 met the inclusion criteria. The research encompassed extensive AI applications in neonatal critical care, employed for prognosis, diagnosis, and therapy strategizing. Artificial neural networks, machine learning, deep learning, and shallow hybrid neural networks were the commonly utilized AI techniques (neonatal critical care). These methods were applied to screen for inborn metabolic abnormalities, predict various outcomes, including death and sepsis, identify diseases such as sepsis, and assess neurodevelopmental outcomes in preterm newborns, helping plan several medical treatments. The included research demonstrated encouraging outcomes when using AI in neonatal critical care. AI-driven electronic arrangements upgrade neonatal basic care by improving risk forecast, promising critical commitments to future health care. Be that as it may, careful appraisal, evidence-based considers, and determination of safety, ethics, and information straightforwardness issues are essential before implementation. Acceptance by administrative bodies and the therapeutic community pivots on tending to these concerns.