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Leveraging Artificial Intelligence for decision support in neonatal and pediatric pharmacotherapy: A scoping review
Luana Conte1, Nunzia Decembrino2, Cristina Arribas3
1Department of Physics and Chemistry, Università degli Studi di Palermo, viale delle Scienze, Palermo, 90128, Italy; Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Local Health Authority (ASL) piazza Filippo Muratori 1, 73100, Lecce, Italy.
Abstract:
The use of Artificial Intelligence (AI) has the potential to transform healthcare in part by enhancing the accuracy of drug dosing and improving patient safety. However, its use in neonatology and pediatrics has just been started, with limited research exploring its full potential. This scoping review systematically maps the literature on AI applications in pediatric and neonatal pharmacology, analyzing studies published between 2004 and 2024. Searches in databases including MEDLINE, Scopus, and IEEE Xplore identified 412 records, of which 33 met the inclusion criteria. These included neonates (n = 8) and older pediatric patients (n = 25), encompassing 58,864 patients and utilizing various Machine-Learning techniques. The use of AI has demonstrated significant potential for precision dosing, predicting drug efficacy, and decreasing the occurrence of adverse events. Despite these promising findings, however, more rigorous, large-scale studies are essential to validate the results. Future research should prioritize real-world applications and address integration barriers, ensuring safe and effective use of AI in neonatal and pediatric clinical practice.
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