Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam.
Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA.
Int J Environ Res Public Health. 2019 Jun 18;16(12):2150. doi: 10.3390/ijerph16122150.
Artificial intelligence (AI)-based techniques have been widely applied in depression research and treatment. Nonetheless, there is currently no systematic review or bibliometric analysis in the medical literature about the applications of AI in depression. We performed a bibliometric analysis of the current research landscape, which objectively evaluates the productivity of global researchers or institutions in this field, along with exploratory factor analysis (EFA) and latent dirichlet allocation (LDA). From 2010 onwards, the total number of papers and citations on using AI to manage depressive disorder have risen considerably. In terms of global AI research network, researchers from the United States were the major contributors to this field. Exploratory factor analysis showed that the most well-studied application of AI was the utilization of machine learning to identify clinical characteristics in depression, which accounted for more than 60% of all publications. Latent dirichlet allocation identified specific research themes, which include diagnosis accuracy, structural imaging techniques, gene testing, drug development, pattern recognition, and electroencephalography (EEG)-based diagnosis. Although the rapid development and widespread use of AI provide various benefits for both health providers and patients, interventions to enhance privacy and confidentiality issues are still limited and require further research.
人工智能（AI）技术已广泛应用于抑郁研究和治疗中。然而，目前医学文献中尚无关于 AI 在抑郁症中的应用的系统评价或文献计量分析。我们对当前研究领域进行了文献计量分析，客观评估了该领域全球研究人员或机构的生产力，同时进行了探索性因子分析（EFA）和潜在狄利克雷分配（LDA）。自 2010 年以来，使用 AI 管理抑郁症的论文数量和引用量大幅增加。就全球 AI 研究网络而言，来自美国的研究人员是该领域的主要贡献者。探索性因子分析表明，AI 应用研究最集中的领域是利用机器学习来识别抑郁症的临床特征，占所有出版物的 60%以上。潜在狄利克雷分配确定了特定的研究主题，包括诊断准确性、结构成像技术、基因测试、药物开发、模式识别和基于脑电图（EEG）的诊断。虽然 AI 的快速发展和广泛应用为医疗保健提供者和患者提供了各种好处，但增强隐私和保密性问题的干预措施仍然有限，需要进一步研究。
