Department of Neonatal and Pediatric Intensive Care, Division of Neonatology, Erasmus MC, University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands.
Datahub, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
Intensive Care Med. 2024 Nov;50(11):1767-1777. doi: 10.1007/s00134-024-07629-8. Epub 2024 Sep 12.
Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benefits is necessary to improve neonatal and pediatric care for critically ill patients. This systematic review seeks to assess the maturity of AI models in neonatal and pediatric intensive care unit (NICU and PICU) treatment, and their risk of bias and objectives.
We conducted a systematic search in Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar. Studies using AI models during NICU or PICU stay were eligible for inclusion. Study design, objective, dataset size, level of validation, risk of bias, and technological readiness of the models were extracted.
Out of the 1257 identified studies 262 were included. The majority of studies was conducted in the NICU (66%) and most had a high risk of bias (77%). An insufficient sample size was the main cause for this high risk of bias. No studies were identified that integrated an AI model in routine clinical practice and the majority of the studies remained in the prototyping and model development phase.
The majority of AI models remain within the testing and prototyping phase and have a high risk of bias. Bridging the gap between designing and clinical implementation of AI models is needed to warrant safe and trustworthy AI models. Specific guidelines and approaches can help improve clinical outcome with usage of AI.
尽管人工智能 (AI) 模型有望提高患者的治疗效果并支持临床决策，但在床边临床应用 AI 模型仍然有限。将 AI 研究的进展转化为切实的临床益处，对于改善危重新生儿和儿科患者的护理至关重要。本系统综述旨在评估新生儿和儿科重症监护病房 (NICU 和 PICU) 治疗中 AI 模型的成熟度及其偏倚风险和目标。
我们在 Medline ALL、Embase、Web of Science 核心合集、Cochrane 对照试验中心注册库和 Google Scholar 中进行了系统检索。符合纳入标准的研究为在 NICU 或 PICU 住院期间使用 AI 模型的研究。提取研究设计、目标、数据集大小、验证水平、偏倚风险以及模型的技术准备情况。
在确定的 1257 项研究中，有 262 项被纳入。大多数研究是在 NICU 进行的（66%），并且大多数研究的偏倚风险较高（77%）。造成这种高偏倚风险的主要原因是样本量不足。未发现将 AI 模型整合到常规临床实践中的研究，且大多数研究仍处于原型设计和模型开发阶段。
大多数 AI 模型仍处于测试和原型设计阶段，且存在较高的偏倚风险。需要弥合 AI 模型设计和临床实施之间的差距，以确保 AI 模型的安全和可靠。使用 AI 提高临床疗效时，具体的指南和方法有助于改善临床效果。
