Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010-2021: A Systematic Review.

IF 2.1 2区 医学 Q4 MEDICAL INFORMATICS
Swaminathan Kandaswamy, Lindsey A Knake, Adam Dziorny, Sean Hernandez, Allison B McCoy, Lauren M Hess, Evan Orenstein, Mia S White, Eric S Kirkendall, Matthew Molloy, Philip Hagedorn, Naveen Muthu, Avinash Murugan, Jonathan M Beus, Mark Mai, Brooke Luo, Juan Demetrio Chaparro
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引用次数: 0

Abstract

Objective: To review pediatric artificial intelligence (AI) implementation studies from 2010-2021 and analyze reported performance measures.

Methods: 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 to 10/2021; must have agency (AI must provide guidance that affects clinical care, not merely running in background). We extracted study characteristics, target users, implementation setting, time span, and performance measures.

Results: 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 to 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.

Conclusions: 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.

2010-2021年儿科预测人工智能在临床实践中的应用:系统综述
目的:回顾2010-2021年儿科人工智能(AI)实施研究,并分析报告的绩效指标。方法:使用受控词汇检索PubMed/Medline、Embase CINHAL、Cochrane Library CENTRAL、IEEE和Web of Science。纳入标准:在儿科临床环境中进行人工智能干预,从数据中学习(即数据驱动,而不是基于规则),并采取行动,提出针对患者的建议;发布时间为2010年1月至2021年10月;必须有代理(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、实施设置、时间跨度和绩效指标。结果:在126篇全文中,17篇符合纳入标准。8项研究(47%)报告了临床结果和过程测量,6项(35%)报告了过程测量,2项(12%)报告了临床结果。五项研究(30%)报告人工智能的临床结果没有差异,四项研究(24%)报告与对照组相比,临床结果有所改善,两项研究(12%)报告使用人工智能对临床结果有积极影响,但没有正式的比较或对照,一项研究(6%)报告人工智能的临床结果较差。12项研究(71%)报告了过程测量的改善,而2项研究(12%)报告没有改善。五项(30%)研究报告了至少一项人类表现测量。结论: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很少,没有对结果、护理过程或人类绩效指标进行标准化报告。更全面的评价将有助于阐明影响机制。
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来源期刊
Applied Clinical Informatics
Applied Clinical Informatics MEDICAL INFORMATICS-
CiteScore
4.60
自引率
24.10%
发文量
132
期刊介绍: ACI is the third Schattauer journal dealing with biomedical and health informatics. It perfectly complements our other journals Öffnet internen Link im aktuellen FensterMethods of Information in Medicine and the Öffnet internen Link im aktuellen FensterYearbook of Medical Informatics. The Yearbook of Medical Informatics being the “Milestone” or state-of-the-art journal and Methods of Information in Medicine being the “Science and Research” journal of IMIA, ACI intends to be the “Practical” journal of IMIA.
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