TY - JOUR
T1 - AI models in clinical neonatology
T2 - a review of modeling approaches and a consensus proposal for standardized reporting of model performance
AU - Husain, Ameena
AU - Knake, Lindsey
AU - Sullivan, Brynne
AU - Barry, James
AU - Beam, Kristyn
AU - Holmes, Emma
AU - Hooven, Thomas
AU - McAdams, Ryan
AU - Moreira, Alvaro
AU - Shalish, Wissam
AU - Vesoulis, Zachary
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc 2024.
PY - 2024
Y1 - 2024
N2 - Artificial intelligence (AI) is a rapidly advancing area with growing clinical applications in healthcare. The neonatal intensive care unit (NICU) produces large amounts of multidimensional data allowing AI and machine learning (ML) new avenues to improve early diagnosis, enhance monitoring, and provide highly-targeted treatment approaches. In this article, we review recent clinical applications of AI to important neonatal problems, including sepsis, retinopathy of prematurity, bronchopulmonary dysplasia, and others. For each clinical area, we highlight a variety of ML models published in the literature and examine the future role they may play at the bedside. While the development of these models is rapidly expanding, a fundamental understanding of model selection, development, and performance evaluation is crucial for researchers and healthcare providers alike. As AI plays an increasing role in daily practice, understanding the implications of AI design and performance will enable more effective implementation. We provide a comprehensive explanation of the AI development process and recommendations for a standardized performance metric framework. Additionally, we address critical challenges, including model generalizability, ethical considerations, and the need for rigorous performance monitoring to avoid model drift. Finally, we outline future directions, emphasizing the importance of collaborative efforts and equitable access to AI innovations.
AB - Artificial intelligence (AI) is a rapidly advancing area with growing clinical applications in healthcare. The neonatal intensive care unit (NICU) produces large amounts of multidimensional data allowing AI and machine learning (ML) new avenues to improve early diagnosis, enhance monitoring, and provide highly-targeted treatment approaches. In this article, we review recent clinical applications of AI to important neonatal problems, including sepsis, retinopathy of prematurity, bronchopulmonary dysplasia, and others. For each clinical area, we highlight a variety of ML models published in the literature and examine the future role they may play at the bedside. While the development of these models is rapidly expanding, a fundamental understanding of model selection, development, and performance evaluation is crucial for researchers and healthcare providers alike. As AI plays an increasing role in daily practice, understanding the implications of AI design and performance will enable more effective implementation. We provide a comprehensive explanation of the AI development process and recommendations for a standardized performance metric framework. Additionally, we address critical challenges, including model generalizability, ethical considerations, and the need for rigorous performance monitoring to avoid model drift. Finally, we outline future directions, emphasizing the importance of collaborative efforts and equitable access to AI innovations.
UR - http://www.scopus.com/inward/record.url?scp=85212187801&partnerID=8YFLogxK
U2 - 10.1038/s41390-024-03774-4
DO - 10.1038/s41390-024-03774-4
M3 - Article
C2 - 39681669
AN - SCOPUS:85212187801
SN - 0031-3998
JO - Pediatric research
JF - Pediatric research
M1 - 100335
ER -