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注意力缺陷多动障碍中的人工智能：关于研究热点、趋势及临床应用的全球视角

Artificial intelligence in ADHD: a global perspective on research hotspots, trends and clinical applications.

作者信息

Wang Xiaofang, Jia Qianfang, Liang Lvyuan, Zhou Weiwei, Yang Weihua, Mu Jingfeng

机构信息

Hebei University of Chinese Medicine, Shijiazhuang, China.

Department of Children's Rehabilitation, First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.

出版信息

Front Hum Neurosci. 2025 Apr 10;19:1577585. doi: 10.3389/fnhum.2025.1577585. eCollection 2025.

DOI:10.3389/fnhum.2025.1577585
PMID:40276113
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12018397/
Abstract

BACKGROUND: Artificial Intelligence (AI), has garnered attention in research on attention deficit hyperactivity disorder (ADHD). In the future, AI may have clinical applications in ADHD, particularly in facilitating the objective diagnosis and classification of ADHD. This study aimed to comprehensively analyze the current status and research frontiers of AI applications in ADHD, identifying hotspots and trends to guide future research directions and promote clinical advancements in this field. METHODS: Articles in the field of AI applications in ADHD were from the Web of Science Core Collection (WoSCC) database. Analysis was conducted using CiteSpace 6.3.R.1. Additionally, high-impact articles were analyzed. RESULTS: A total of 342 articles from 50 countries and regions were included. The United States led with 103 articles, having the highest H-index of 21, followed by China with 69 articles, and England with 34 articles. The State University of New York System produced the most articles (11), and had the most articles (12). Burst keywords in 2022-2024 included "diagnosis," "network," "attention deficit hyperactivity disorder" and "artificial intelligence." CONCLUSION: AI technologies have become a prominent topic in ADHD research, with the United States, China, and England leading in articles and influence. The State University of New York System was the most influential institution, while stood out as the key journal. Utilizing networks and other AI technologies for diagnosing ADHD represents current hotspots and future trends, potentially offering objective indicators for ADHD.

摘要

背景：人工智能（AI）在注意力缺陷多动障碍（ADHD）研究中已受到关注。未来，AI可能在ADHD的临床应用中发挥作用，特别是在促进ADHD的客观诊断和分类方面。本研究旨在全面分析AI在ADHD应用中的现状和研究前沿，确定热点和趋势，以指导未来的研究方向并推动该领域的临床进展。 方法：ADHD领域中AI应用的文章来自科学网核心合集（WoSCC）数据库。使用CiteSpace 6.3.R.1进行分析。此外，还对高影响力文章进行了分析。 结果：共纳入来自50个国家和地区的342篇文章。美国以103篇文章领先，H指数最高为21，其次是中国有69篇文章，英国有34篇文章。纽约州立大学系统发表的文章最多（11篇），且被引用文章最多（12篇）。2022 - 2024年的爆发关键词包括“诊断”、“网络”、“注意力缺陷多动障碍”和“人工智能”。 结论：AI技术已成为ADHD研究中的一个突出主题，美国、中国和英国在文章数量和影响力方面领先。纽约州立大学系统是最具影响力的机构，而[具体期刊名未给出]是关键期刊。利用网络等AI技术诊断ADHD是当前热点和未来趋势，可能为ADHD提供客观指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/5b9d0d1a88d5/fnhum-19-1577585-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/66f65ea0405e/fnhum-19-1577585-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/1fb8a3d075b1/fnhum-19-1577585-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/644a57e0265e/fnhum-19-1577585-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/449dd3e04790/fnhum-19-1577585-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/5b9d0d1a88d5/fnhum-19-1577585-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/66f65ea0405e/fnhum-19-1577585-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/1fb8a3d075b1/fnhum-19-1577585-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/644a57e0265e/fnhum-19-1577585-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/449dd3e04790/fnhum-19-1577585-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/55e0/12018397/5b9d0d1a88d5/fnhum-19-1577585-g005.jpg

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本文引用的文献

[1]
Infant attention and frontal EEG neuromarkers of childhood ADHD.

Dev Cogn Neurosci. 2025-4

[2]
Comparison of attention and brain functional connectivity between patient groups with schizophrenia and attention deficit hyperactivity disorder.

Psychiatry Res. 2025-3

[3]
Detecting noncredible symptomology in ADHD evaluations using machine learning.

J Clin Exp Neuropsychol. 2024-12

[4]
Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine.

J Med Internet Res. 2025-1-7

[5]
Development and research status of intelligent ophthalmology in China.

Int J Ophthalmol. 2024-12-18

[6]
Facial expression analysis using convolutional neural network for drug-naive and chronic schizophrenia.

J Psychiatr Res. 2025-1

[7]
Deconstructing Cognitive Impairment in Psychosis With a Machine Learning Approach.

JAMA Psychiatry. 2025-1-1

[8]
Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in glaucoma from 2013 to 2022.

Int J Ophthalmol. 2024-9-18

[9]
Systematic bibliometric analysis of research hotspots and trends on the application of premium IOLs in the past 2 decades.

Int J Ophthalmol. 2024-4-18

[10]
An ensemble deep learning diagnostic system for determining Clinical Activity Scores in thyroid-associated ophthalmopathy: integrating multi-view multimodal images from anterior segment slit-lamp photographs and facial images.

Front Endocrinol (Lausanne). 2024

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