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Machine learning and deep learning are two subsets of artificial intelligencethat involve teaching computers to learn and make decisions from any sort ofdata. Most recent developments in artificial intelligence are coming from deeplearning, which has proven revolutionary in almost all fields, from computervision to health sciences. The effects of deep learning in medicine havechanged the conventional ways of clinical application significantly. Althoughsome sub-fields of medicine, such as pediatrics, have been relatively slow inreceiving the critical benefits of deep learning, related research inpediatrics has started to accumulate to a significant level, too. Hence, inthis paper, we review recently developed machine learning and deeplearning-based solutions for neonatology applications. We systematicallyevaluate the roles of both classical machine learning and deep learning inneonatology applications, define the methodologies, including algorithmicdevelopments, and describe the remaining challenges in the assessment ofneonatal diseases by using PRISMA 2020 guidelines. To date, the primary areasof focus in neonatology regarding AI applications have included survivalanalysis, neuroimaging, analysis of vital parameters and biosignals, andretinopathy of prematurity diagnosis. We have categorically summarized 106research articles from 1996 to 2022 and discussed their pros and cons,respectively. In this systematic review, we aimed to further enhance thecomprehensiveness of the study. We also discuss possible directions for new AImodels and the future of neonatology with the rising power of AI, suggestingroadmaps for the integration of AI into neonatal intensive care units.

The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence