新生児・小児医学

抽象的な

Machine Translation in Neonatal Medicine Review

Muthuram Perera

While AI clinical decision support tools appear superior to rule-based tools in many situations, their use can pose additional challenges. An example of this is the lack of large data sets and the existence of imbalanced data (e.g. due to low rates of adverse events) commonly observed in neonatal medicine. This paper describes the latest and most powerful applications of AI in neonatal medicine and highlights future research directions relevant to the neonatal population. AI applications currently being tested in neonatology include vital signs monitoring, disease prediction (respiratory distress syndrome, bronchopulmonary dysplasia, apnea of prematurity), risk stratification retinopathy of prematurity, intestinal perforation, jaundice it is included. including tools for neurological diagnosis and prognostic support. New image recognition techniques (especially useful for staging, neuroimaging, etc.) and timely detection of infections. Tools like these that support neonatal doctors in their daily practice could be very revolutionary in the near future. On the other hand, in order to use AI technology correctly, it is important to be aware of its limitations.