Review
Exp. Biol. Med.
Sec. Artificial Intelligence/Machine Learning Applications to Biomedical Research
Volume 250 - 2025 | doi: 10.3389/ebm.2025.10238
This article is part of the IssueExperimental Biology and Medicine Volume 250 Issue 3View all 7 articles
Artificial intelligence for children with attention deficit/hyperactivity disorder: A scoping review
- 1Department of Neonatology, Northern Theater Command General Hospital, Shenyang, China
- 2Post-graduate College, China Medical University, Shenyang, Liaoning Province, China
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Attention deficit/hyperactivity disorder is a common neuropsychiatric disorder that affects around 5%-7% of children worldwide. Artificial intelligence provides advanced models and algorithms for better diagnosis, prediction and classification of attention deficit/hyperactivity disorder. This study aims to explore artificial intelligence models used for the prediction, early diagnosis and classification of attention deficit/hyperactivity disorder as reported in the literature. A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Out of the 1994 publications, 52 studies were included in the scoping review. The included articles reported the use of artificial intelligence for 3 different purposes. Of these included articles, artificial intelligence techniques were mostly used for the diagnosis of attention deficit/hyperactivity disorder (38/52, 79%). Magnetic resonance imaging (20/52, 38%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (28/52, 54%). Machine learning models were the most prominent branch of artificial intelligence used for attention deficit/hyperactivity disorder in the studies, and the support vector machine was the most used algorithm (34/52, 65%). The most commonly used validation in the studies was k-fold cross-validation (34/52, 65%). A higher level of accuracy (98.23%) was found in studies that used Convolutional Neural Networks algorithm. This review provides an overview of research on artificial intelligence models and algorithms for attention deficit/hyperactivity disorder, providing data for further research to support clinical decision-making in healthcare.
Keywords: Artificial intelligence, attention deficit/hyperactivity disorder, machine learning, deep learning, review method
Received: 12 May 2024; Accepted: 28 Mar 2025.
Copyright: © 2025 Sun, Cai, Huang, Li and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Bing Wei, Department of Neonatology, Northern Theater Command General Hospital, Shenyang, China
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