Kandaswamy Swaminathan, Knake Lindsey A, Dziorny Adam C, Hernandez Sean M, McCoy Allison B, Hess Lauren M, Orenstein Evan, White Mia S, Kirkendall Eric S, Molloy Matthew J, Hagedorn Philip A, Muthu Naveen, Murugan Avinash, Beus Jonathan M, Mai Mark, Luo Brooke, Chaparro Juan D
Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.
Division of Neonatology, Department of Pediatrics, University of Iowa, Iowa City, Iowa, United States.
Appl Clin Inform. 2025 May;16(3):477-487. doi: 10.1055/a-2521-1508. Epub 2025 Jan 21.
To review pediatric artificial intelligence (AI) implementation studies from 2010 to 2021 and analyze reported performance measures.We searched PubMed/Medline, Embase CINHAL, Cochrane Library CENTRAL, IEEE, and Web of Science with controlled vocabulary. Inclusion criteria: AI intervention in a pediatric clinical setting that learns from data (i.e., data-driven, as opposed to rule-based) and takes actions to make patient-specific recommendations; published between 01/2010 and 10/2021; must have agency (AI must provide guidance that affects clinical care, not merely running in the background). We extracted study characteristics, target users, implementation setting, time span, and performance measures.Of 126 articles reviewed as full text, 17 met inclusion criteria. Eight studies (47%) reported both clinical outcomes and process measures, six (35%) reported only process measures and two (12%) reported only clinical outcomes. Five studies (30%) reported no difference in clinical outcomes with AI, four (24%) reported improvement in clinical outcomes compared with controls, two (12%) reported positive effects on clinical outcomes with use of AI but had no formal comparison or controls, and one (6%) reported poor clinical outcomes with AI. Twelve studies (71%) reported improvement in process measures, while two (12%) reported no improvement. Five (30%) studies reported on at least 1 human performance measure.While there are many published pediatric AI models, the number of AI implementations is minimal with no standardized reporting of outcomes, care processes, or human performance measures. More comprehensive evaluations will help elucidate mechanisms of impact.
回顾2010年至2021年的儿科人工智能（AI）实施研究，并分析报告的性能指标。我们使用受控词汇在PubMed/Medline、Embase、CINHAL、Cochrane图书馆CENTRAL、IEEE和科学网中进行检索。纳入标准：在儿科临床环境中进行的人工智能干预，该干预从数据中学习（即数据驱动，而非基于规则）并采取行动做出针对患者的建议；2010年1月至2021年10月期间发表；必须具有实际作用（人工智能必须提供影响临床护理的指导，而不仅仅是在后台运行）。我们提取了研究特征、目标用户、实施环境、时间跨度和性能指标。在126篇作为全文审查的文章中，17篇符合纳入标准。八项研究（47%）报告了临床结果和过程指标，六项（35%）仅报告了过程指标，两项（12%）仅报告了临床结果。五项研究（30%）报告人工智能在临床结果方面无差异，四项（24%）报告与对照组相比临床结果有所改善，两项（12%）报告使用人工智能对临床结果有积极影响但未进行正式比较或设置对照组，一项（6%）报告人工智能导致临床结果不佳。十二项研究（71%）报告过程指标有所改善，而两项（12%）报告无改善。五项（30%）研究报告了至少一项人类性能指标。虽然有许多已发表的儿科人工智能模型，但人工智能实施的数量极少，且在结果、护理过程或人类性能指标方面没有标准化报告。更全面的评估将有助于阐明影响机制。
