Husain Ameena, Knake Lindsey, Sullivan Brynne, Barry James, Beam Kristyn, Holmes Emma, Hooven Thomas, McAdams Ryan, Moreira Alvaro, Shalish Wissam, Vesoulis Zachary
Division of Neonatology, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT, USA.
Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, IA, USA.
Pediatr Res. 2024 Dec 17. doi: 10.1038/s41390-024-03774-4.
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.
人工智能（AI）是一个快速发展的领域，在医疗保健中的临床应用不断增加。新生儿重症监护病房（NICU）产生大量多维度数据，为人工智能和机器学习（ML）改善早期诊断、加强监测以及提供高度针对性的治疗方法开辟了新途径。在本文中，我们回顾了人工智能在重要新生儿问题上的近期临床应用，包括败血症、早产儿视网膜病变、支气管肺发育不良等。对于每个临床领域，我们重点介绍了文献中发表的各种机器学习模型，并探讨了它们在床边可能发挥的未来作用。虽然这些模型的发展正在迅速扩展，但对模型选择、开发和性能评估的基本理解对研究人员和医疗保健提供者都至关重要。随着人工智能在日常实践中发挥越来越重要的作用，了解人工智能设计和性能的影响将有助于更有效地实施。我们对人工智能开发过程进行了全面解释，并为标准化性能指标框架提供了建议。此外，我们还讨论了关键挑战，包括模型的通用性、伦理考量以及进行严格性能监测以避免模型漂移的必要性。最后，我们概述了未来方向，强调了合作努力以及公平获取人工智能创新的重要性。
