From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit,Intensive Care Medicine

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From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit
Intensive Care Medicine ( IF 27.1 ) Pub Date : 2024-09-12 , DOI: 10.1007/s00134-024-07629-8
Janno S Schouten _{1,

2} , Melissa A C M Kalden _{1,

2,

3} , Eris van Twist ₄ , Irwin K M Reiss ₁ , Diederik A M P J Gommers _{2,

5} , Michel E van Genderen _{2,

5} , H Rob Taal _{1,

2}

Affiliation

Purpose

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.

Methods

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.

Results

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.

Conclusion

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.

中文翻译：

从字节到床边：人工智能在新生儿和儿科重症监护病房的使用和准备情况的系统评价

目的

尽管人工智能（AI）模型有望提高患者预后并支持临床决策，但人工智能（AI）模型在床边的临床使用仍然有限。将 AI 研究的进步转化为切实的临床益处对于改善危重患者的新生儿和儿科护理是必要的。本系统综述旨在评估 AI 模型在新生儿和儿科重症监护病房（NICU 和 PICU）治疗中的成熟度，以及它们的偏倚风险和目标。

方法

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。在 NICU 或 PICU 住院期间使用 AI 模型的研究符合纳入条件。提取模型的研究设计、目标、数据集大小、验证水平、偏倚风险和技术准备情况。