Emerging trends in Alzheimer's disease diagnosis and prediction using artificial intelligence: A bibliometric analysis of the top cited 100 articles.

ABSTRACT:

Background

AD is a significant public health challenge, and AI technologies, including deep learning and machine learning, offer the potential to dramatically improve diagnostic and predictive accuracy. These technologies are widely applied in AD research. However, comprehensive literature summaries of this field remain limited. This study uses bibliometric analysis to examine research hotspots, trends, future development potential, and limitations in AI-based AD diagnosis and prediction.

Methods

We conducted a bibliometric analysis of 100 top cited studies on AI-based diagnosis and prediction of AD, using data from the WoSCC. We performed the analysis using CiteSpace 6.3.R2, VOSviewer 1.6.19, Scimago Graphica 1.0.39, Microsoft Excel 2021, and R package Bibliometrix running

SUBMITTER: Zhang G 

PROVIDER: S-EPMC12276523 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature