# Applications of Artificial Intelligence in Neonatology
## 3 Discussion  
Artificial intelligence has great potential to improve our understanding of disease and to enhance therapeutic effectiveness--in neonatal medicine as well as elsewhere--as reported in recent studies. We presented the most relevant studies in this field and found that the most important applications of AI involved neuromonitoring, assessment, and prediction of significant respiratory diseases and prediction of retinopathy of prematurity, neonatal sepsis, jaundice, and intestinal perforation. These conditions represent serious complications of prematurity and may lead to permanent consequences and even death. The development of point-of-care tools is another promising approach, enabling cliniciansto predict and effectively treat some conditions, such as RDS. In any case, there are some ethical challenges in the application of AI to neonatal clinical care. Permission to participate is typically provided by parents acting as legally authorized representatives since neonates are not capable of consenting. Considering the need for proxy consent, newborn infants need additional protection as research participants. Furthermore, parents of sick or premature infants have to face numerous difficulties in the early weeks and months of life of their babies. Mental, physical, and emotional fatigue may impair the capacity of parents to provide informed consent. Hence, particular attention has been given to the respect for rigorous ethical standards in perinatal research [83]. There are also several ethical and jurisdictional pitfalls regarding the application of ML in clinical decision-making. For example, if the validated algorithm were to make a mistake, who should be considered accountable for it? Other limitations of AI include the need for large datasets to \"train\" the models, the presence of unbalanced data, issues of generalizability, a lack of evidence-based care for some neonatal conditions, maturational variation, and costs. Further important issues to be faced when applying AI to neonatal care are the need for proper performance assessment, the need for external validation to enhance generalizability, and the interpretability of the model. The results of the studies presented in our manuscript are promising. Moreover, the majority of the diseases we discussed represent serious neonatal conditions and may lead to permanent consequences and even death. The prevalence of the diseases discussed in this narrative review, along with estimates of related direct and indirect costs, have been previously reported [84; 85; 86] and, in our opinion, justify including the assessment of AI on the research agenda.  
Conceptualization, S.N.; methodology, R.C., A.S. and S.N.; writing--original draft preparation, R.C., A.S. and S.N.; writing--review and editing, S.N., R.C., A.S., M.L.P., A.P. and G.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Ethical review and approval were waived for this study due to the nature of the manuscript. Not applicable.  
No new data were created. We thank [PERSON], [PERSON], and [PERSON] for providing us with insightful images. The authors declare no conflict of interest.