부산대학교 도서관

The potential of artificial intelligence (AI) methods for the early detection of neonatal jaundice is examined in this study, a common condition affecting newborns. Through comprehensive research, we discover that AI models, notably deep learning algorithms, exhibit remarkable accuracy in identifying neonates at risk of jaundice well before visible symptoms manifest or bilirubin levels become critically high. Early prediction is of paramount importance in neonatal jaundice, as it allows for timely interventions, reducing the risk of complications, minimizing invasive treatments, and easing emotional and financial burdens on families. The implications of this research extend to a brighter and healthier future for newborns, with AI technologies at the forefront of safeguarding their well-being from the outset of life. We also discuss potential future research directions, emphasizing the importance of data quality, generalizability across diverse populations, and ethical considerations for responsible AI implementation in neonatal care.