Fortunov Regine M, Cabacungan Erwin, Barry James S, Jagarapu Jawahar
Division of Neonatology, Baylor College of Medicine, Houston, TX, United States.
Section of Neonatology, Medical College of Wisconsin, Milwaukee, WI, United States.
Semin Perinatol. 2024 Dec;48(8):151992. doi: 10.1016/j.semperi.2024.151992. Epub 2024 Oct 11.
Neonatal intensive care unit resuscitative care continually evolves and increasingly relies on data. Data driven precision resuscitation care can be enabled by leveraging informatics tools and artificial intelligence. Despite technological advancements, these data are often underutilized due to suboptimal data capture, aggregation, and low adoption of artificial intelligence and analytic tools. This review describes the fundamentals and explores the evidence behind informatics and artificial intelligence tools supporting neonatal intensive care unit resuscitative care, training and education. Key findings include the need for effective interface design for accurate data capture followed by storage and translation to wisdom using analytics and artificial intelligence tools. This review addresses the issues of data privacy, bias, liability and ethical frameworks when adopting these tools. While these emerging technologies hold great promise to improve resuscitation, further study of these applications in neonatal population and awareness of informatics and artificial intelligence principles among clinicians is imperative.
新生儿重症监护病房的复苏护理不断发展，且越来越依赖数据。借助信息学工具和人工智能可实现数据驱动的精准复苏护理。尽管有技术进步，但由于数据采集欠佳、数据汇总问题以及人工智能和分析工具的采用率较低，这些数据往往未得到充分利用。本综述描述了相关基本原理，并探讨了支持新生儿重症监护病房复苏护理、培训和教育的信息学及人工智能工具背后的证据。主要发现包括需要进行有效的界面设计以准确采集数据，随后进行存储，并利用分析和人工智能工具将其转化为实用信息。本综述讨论了采用这些工具时的数据隐私、偏差、责任和伦理框架问题。虽然这些新兴技术有望改善复苏效果，但必须进一步研究这些应用在新生儿群体中的情况，并提高临床医生对信息学和人工智能原理的认识。
