{"title":"人工智能在抑郁症检测和诊断中的应用的文献计量学和视觉分析:趋势和未来方向。","authors":"Wenbo Ren, Xiali Xue, Lu Liu, Jiahuan Huang","doi":"10.2196/79293","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Depression is a highly prevalent and debilitating mental disorder, yet its diagnosis heavily relies on subjective assessments, leading to challenges in accuracy and consistency. The rapid advancements in Artificial Intelligence (AI) offer promising avenues for more objective and efficient diagnostic approaches. Understanding the evolving landscape of AI applications in depression diagnosis is crucial for guiding future research and clinical translation.</p><p><strong>Objective: </strong>This study aims to provide a comprehensive bibliometric and visual analysis of the global research trends, intellectual structure, and emerging frontiers in the application of AI for depression detection and diagnosis from 2015 to 2024.</p><p><strong>Methods: </strong>A systematic literature search was conducted on the Web of Science Core Collection (WoSCC) database to identify relevant publications on AI applications in depression diagnosis from January 1, 2015, to December 31, 2024. A total of 2304 articles were retrieved and analyzed using bibliometric software CiteSpace. The analysis encompassed temporal trends, keyword dynamics, author collaboration networks, institutional influence, country contributions, and intellectual foundations through co-citation analysis of journals and references.</p><p><strong>Results: </strong>The field exhibited exponential growth in publications and citations, particularly after 2018, reflecting increasing academic and clinical interest. Key thematic shifts were observed from traditional machine learning to advanced deep learning, multimodal fusion, and the integration of objective biomarkers (e.g., EEG, facial expressions). Leading contributors included institutions from China and the United States, with forming collaborative bridges from countries like Canada and Singapore. The intellectual base is highly interdisciplinary, drawing heavily from computer science, neuroscience, and psychiatry, with a notable surge in engineering and translational research.</p><p><strong>Conclusions: </strong>The integration of AI in depression diagnosis is a rapidly maturing and diversifying field, transitioning from theoretical exploration to clinically relevant applications focusing on objective, data-driven approaches. The identified trends underscore the need for enhanced interdisciplinary and international collaboration, ethical framework development, and a focus on translating technological innovations into accessible and equitable mental health solutions. These findings offer valuable insights for researchers, clinicians, and policymakers to strategically advance AI-assisted depression diagnostics globally.</p><p><strong>Clinicaltrial: </strong></p>","PeriodicalId":48616,"journal":{"name":"Jmir Mental Health","volume":" ","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Bibliometric and Visual Analysis of Artificial Intelligence Applications in Depression Detection and Diagnosis: Trends and Future Directions.\",\"authors\":\"Wenbo Ren, Xiali Xue, Lu Liu, Jiahuan Huang\",\"doi\":\"10.2196/79293\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Depression is a highly prevalent and debilitating mental disorder, yet its diagnosis heavily relies on subjective assessments, leading to challenges in accuracy and consistency. The rapid advancements in Artificial Intelligence (AI) offer promising avenues for more objective and efficient diagnostic approaches. Understanding the evolving landscape of AI applications in depression diagnosis is crucial for guiding future research and clinical translation.</p><p><strong>Objective: </strong>This study aims to provide a comprehensive bibliometric and visual analysis of the global research trends, intellectual structure, and emerging frontiers in the application of AI for depression detection and diagnosis from 2015 to 2024.</p><p><strong>Methods: </strong>A systematic literature search was conducted on the Web of Science Core Collection (WoSCC) database to identify relevant publications on AI applications in depression diagnosis from January 1, 2015, to December 31, 2024. A total of 2304 articles were retrieved and analyzed using bibliometric software CiteSpace. The analysis encompassed temporal trends, keyword dynamics, author collaboration networks, institutional influence, country contributions, and intellectual foundations through co-citation analysis of journals and references.</p><p><strong>Results: </strong>The field exhibited exponential growth in publications and citations, particularly after 2018, reflecting increasing academic and clinical interest. Key thematic shifts were observed from traditional machine learning to advanced deep learning, multimodal fusion, and the integration of objective biomarkers (e.g., EEG, facial expressions). Leading contributors included institutions from China and the United States, with forming collaborative bridges from countries like Canada and Singapore. The intellectual base is highly interdisciplinary, drawing heavily from computer science, neuroscience, and psychiatry, with a notable surge in engineering and translational research.</p><p><strong>Conclusions: </strong>The integration of AI in depression diagnosis is a rapidly maturing and diversifying field, transitioning from theoretical exploration to clinically relevant applications focusing on objective, data-driven approaches. The identified trends underscore the need for enhanced interdisciplinary and international collaboration, ethical framework development, and a focus on translating technological innovations into accessible and equitable mental health solutions. These findings offer valuable insights for researchers, clinicians, and policymakers to strategically advance AI-assisted depression diagnostics globally.</p><p><strong>Clinicaltrial: </strong></p>\",\"PeriodicalId\":48616,\"journal\":{\"name\":\"Jmir Mental Health\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jmir Mental Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/79293\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jmir Mental Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/79293","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
摘要
背景:抑郁症是一种非常普遍和令人衰弱的精神障碍,但其诊断严重依赖于主观评估,导致准确性和一致性面临挑战。人工智能(AI)的快速发展为更客观、更有效的诊断方法提供了有希望的途径。了解人工智能在抑郁症诊断中的应用前景对于指导未来的研究和临床转化至关重要。目的:本研究旨在对2015 - 2024年AI在抑郁症检测与诊断中的应用的全球研究趋势、知识结构和新兴前沿进行全面的文献计量和视觉分析。方法:系统检索Web of Science Core Collection (WoSCC)数据库2015年1月1日至2024年12月31日期间有关AI在抑郁症诊断中的应用的相关文献。使用文献计量学软件CiteSpace共检索和分析了2304篇文献。分析包括时间趋势、关键词动态、作者合作网络、机构影响、国家贡献以及通过期刊和参考文献的共引分析的知识基础。结果:该领域的出版物和引用呈指数级增长,特别是在2018年之后,反映了越来越多的学术和临床兴趣。观察到从传统机器学习到高级深度学习、多模态融合和客观生物标志物(如脑电图、面部表情)的集成的关键主题转变。主要贡献者包括来自中国和美国的机构,并与加拿大和新加坡等国建立了合作桥梁。它的知识基础是高度跨学科的,从计算机科学、神经科学和精神病学中汲取了大量的知识,在工程和转化研究方面也有显著的增长。结论:人工智能在抑郁症诊断中的集成是一个快速成熟和多元化的领域,从理论探索转向以客观、数据驱动的方法为重点的临床相关应用。所确定的趋势强调需要加强跨学科和国际合作,制定道德框架,并注重将技术创新转化为可获得和公平的精神卫生解决办法。这些发现为研究人员、临床医生和政策制定者在全球范围内战略性地推进人工智能辅助抑郁症诊断提供了宝贵的见解。临床试验:
A Bibliometric and Visual Analysis of Artificial Intelligence Applications in Depression Detection and Diagnosis: Trends and Future Directions.
Background: Depression is a highly prevalent and debilitating mental disorder, yet its diagnosis heavily relies on subjective assessments, leading to challenges in accuracy and consistency. The rapid advancements in Artificial Intelligence (AI) offer promising avenues for more objective and efficient diagnostic approaches. Understanding the evolving landscape of AI applications in depression diagnosis is crucial for guiding future research and clinical translation.
Objective: This study aims to provide a comprehensive bibliometric and visual analysis of the global research trends, intellectual structure, and emerging frontiers in the application of AI for depression detection and diagnosis from 2015 to 2024.
Methods: A systematic literature search was conducted on the Web of Science Core Collection (WoSCC) database to identify relevant publications on AI applications in depression diagnosis from January 1, 2015, to December 31, 2024. A total of 2304 articles were retrieved and analyzed using bibliometric software CiteSpace. The analysis encompassed temporal trends, keyword dynamics, author collaboration networks, institutional influence, country contributions, and intellectual foundations through co-citation analysis of journals and references.
Results: The field exhibited exponential growth in publications and citations, particularly after 2018, reflecting increasing academic and clinical interest. Key thematic shifts were observed from traditional machine learning to advanced deep learning, multimodal fusion, and the integration of objective biomarkers (e.g., EEG, facial expressions). Leading contributors included institutions from China and the United States, with forming collaborative bridges from countries like Canada and Singapore. The intellectual base is highly interdisciplinary, drawing heavily from computer science, neuroscience, and psychiatry, with a notable surge in engineering and translational research.
Conclusions: The integration of AI in depression diagnosis is a rapidly maturing and diversifying field, transitioning from theoretical exploration to clinically relevant applications focusing on objective, data-driven approaches. The identified trends underscore the need for enhanced interdisciplinary and international collaboration, ethical framework development, and a focus on translating technological innovations into accessible and equitable mental health solutions. These findings offer valuable insights for researchers, clinicians, and policymakers to strategically advance AI-assisted depression diagnostics globally.
期刊介绍:
JMIR Mental Health (JMH, ISSN 2368-7959) is a PubMed-indexed, peer-reviewed sister journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175).
JMIR Mental Health focusses on digital health and Internet interventions, technologies and electronic innovations (software and hardware) for mental health, addictions, online counselling and behaviour change. This includes formative evaluation and system descriptions, theoretical papers, review papers, viewpoint/vision papers, and rigorous evaluations.