Kerth Janna-Lina, Hagemeister Maurus, Bischops Anne C, Reinhart Lisa, Dukart Juergen, Heinrichs Bert, Eickhoff Simon B, Meissner Thomas
Dept. of General Pediatrics, Pediatric Cardiology and Neonatology, Medical Faculty, University Children's Hospital Düsseldorf, Heinrich Heine University, Moorenstr. 5, 40227, Düsseldorf, Germany.
Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
Eur J Pediatr. 2024 Dec 14;184(1):83. doi: 10.1007/s00431-024-05846-3.
The integration of artificial intelligence (AI) and machine learning (ML) has shown potential for various applications in the medical field, particularly for diagnosing and managing chronic diseases among children and adolescents. This systematic review aims to comprehensively analyze and synthesize research on the use of AI for monitoring, guiding, and assisting pediatric patients with chronic diseases. Five major electronic databases were searched (Medline, Scopus, PsycINFO, ACM, Web of Science), along with manual searches of gray literature, personal archives, and reference lists of relevant papers. All original studies as well as conference abstracts and proceedings, focusing on AI applications for pediatric chronic disease care were included. Thirty-one studies met the inclusion criteria. We extracted AI method used, study design, population, intervention, and main results. Two researchers independently extracted data and resolved discrepancies through discussion. AI applications are diverse, encompassing, e.g., disease classification, outcome prediction, or decision support. AI generally performed well, though most models were tested on retrospective data. AI-based tools have shown promise in mental health analysis, e.g., by using speech sampling or social media data to predict therapy outcomes for various chronic conditions.
While AI holds potential in pediatric chronic disease care, most reviewed studies are small-scale research projects. Prospective clinical implementations are needed to validate its effectiveness in real-world scenarios. Ethical considerations, cultural influences, and stakeholder attitudes should be integrated into future research.
• Artificial Intelligence (AI) will play a more dominant role in medicine and healthcare in the future and many applications are already being developed.
• Our review provides an overview on how AI-driven systems might be able to support children and adolescents with chronic illnesses. • While many applications are being researched, few have been tested on real-world, prospective, clinical data.
人工智能(AI)与机器学习(ML)的整合已在医学领域的各种应用中展现出潜力,尤其是在诊断和管理儿童及青少年的慢性病方面。本系统评价旨在全面分析和综合关于使用人工智能监测、指导和协助患有慢性病的儿科患者的研究。我们检索了五个主要的电子数据库(Medline、Scopus、PsycINFO、ACM、科学网),并手动检索了灰色文献、个人存档以及相关论文的参考文献列表。纳入了所有聚焦于人工智能在儿科慢性病护理中应用的原创研究以及会议摘要和论文集。31项研究符合纳入标准。我们提取了所使用的人工智能方法、研究设计、研究对象、干预措施和主要结果。两名研究人员独立提取数据,并通过讨论解决差异。人工智能的应用多种多样,包括疾病分类、结果预测或决策支持等。人工智能总体表现良好,不过大多数模型是在回顾性数据上进行测试的。基于人工智能的工具在心理健康分析方面显示出前景,例如通过使用语音采样或社交媒体数据来预测各种慢性病的治疗结果。
虽然人工智能在儿科慢性病护理中具有潜力,但大多数综述研究都是小规模的研究项目。需要进行前瞻性临床实施以验证其在现实场景中的有效性。未来的研究应纳入伦理考量、文化影响和利益相关者的态度。
• 人工智能(AI)未来将在医学和医疗保健中发挥更主导的作用,并且许多应用已经在开发中。
• 我们的综述概述了人工智能驱动的系统如何能够支持患有慢性病的儿童和青少年。• 虽然正在研究许多应用,但很少有在真实世界的前瞻性临床数据上进行测试的。