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You, J., Zhang, Y., Wang, H., Yang, M., Feng, J., Yu, J., et al. (2022) Development of a Novel Dementia Risk Prediction Model in the General Population: A Large, Longitudinal, Population-Based Machine-Learning Study. eClinicalMedicine, 53, Article ID: 101665.
https://doi.org/10.1016/j.eclinm.2022.101665

has been cited by the following article:

• TITLE: Application of Artificial Intelligence in Dementia: A Bibliometric Analysis

  AUTHORS: Miao Wan, Mi Liu, Xiong Ke

  KEYWORDS: Artificial Intelligence, Dementia, Machine Learning, Bibliometric Analysis, CiteSpace

  JOURNAL NAME: Open Access Library Journal, Vol.12 No.12, December 15, 2025

  ABSTRACT: Based on 1526 academic journal papers from the Web of Science Core Collection between 2005 and 2024, this study used CiteSpace and Excel to conduct a bibliometric analysis of the application of artificial intelligence in dementia research. Research shows that the number of publications in this field has grown significantly since 2018. The United States, China, and the United Kingdom are the main contributing countries, with the University of London and the University of California System being the most productive institutions. Key research hotspots include identification of dementia stages, early diagnostic screening, risk prediction, and disease progression forecasting. Deep learning has become a core technology in this field, while “natural language processing” represents a current research frontier. Future trends will focus on multimodal data integration and deep neural networks to support precise prediction and intervention of dementia.