Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the “Baby NINJA” model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the “STARZ” risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

急性肾损伤（AKI）对儿科和新生儿患者的短期和长期临床结果具有重大影响，这些人群必须减少导致 AKI 的途径，并为早期诊断和治疗干预做好准备已建立的 AKI。最近，人工智能 (AI) 为利用机器学习 (ML) 的 AKI 早期检测/预测提供了更多出现预测模型。通过提供来自风险评分和电子警报的强有力的细节和证据，本综述概述了对儿科/新生儿 AKI 中 AI 现状的全面、整体的见解。在儿科人群中，包括 XGBoost、逻辑回归、支持向量机、决策树、朴素贝叶斯和风险分层评分（肾心绞痛指数 (RAI)、即时行动消除肾毒性损伤 (NINJA)）在内的 AI 模型已经使用血清肌酐、尿量和电子健康记录 (EHR) 警报等变量成功预测 AKI。同样，在新生儿人群中，使用“Baby NINJA”模型显示肾毒性药物暴露减少了 42%，AKI 发生率减少了 78%，AKI 天数减少了 68%。此外，“STARZ”风险分层 AI 模型显示了 NICU 入院 7 天内 AKI 的预测能力，在验证队列和推导队列中，AUC 分别为 0.93 和 0.96。许多研究报告了使用生物标志物预测儿科患者和新生儿 AKI 的优越性。未来的方向包括在 Labelbox 配置中应用 AI 和生物标志物（NGAL、CysC、OPN、IL-18、B2M 等），以创建更稳健、更准确的模型来预测和检测儿科/新生儿 AKI。