ArticleMedicine2026
The global research of artificial intelligence on Alzheimer disease: A 25-year bibliometric analysis.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the increasing global prevalence of Alzheimer disease (AD), there has been heightened attention on the onset and progression of AD. Studies indicate that artificial intelligence (AI) has demonstrated potential in the early prediction and diagnosis of AD. However, current research still falls short in terms of data diversity and the application of personalized models.
objectiveThis manuscript utilizes bibliometric methods to explore the application trends and emerging frontiers of AI in AD research.
methodsIn the Web of Science Core Collection, we gathered documents from 1999 to 2023 focusing on the application of AI in AD. CiteSpace and VOSviewer were utilized to conduct a thorough analysis of various aspects, including countries, institutions, authors, journals, and keywords.
resultsA total of 5347 articles were selected for this study. The United States of America leads this field. The University of London, Harvard University, the University of North Carolina, and the University of California are the top 4 institutions by publication volume. Daoqiang Zhang is identified as the most influential scholar in this field. NeuroImage is regarded as the most influential journal in this field. The keyword co-occurrence analysis indicates that this study focuses on the application of machine learning and deep learning (DL) in predicting and diagnosing AD. Research trends show an increasing preference for using DL in combination with multimodal data for AD classification and early diagnosis.
conclusionsAI is accelerating AD research through DL and multimodal imaging, driving progress in early diagnosis and biomarker discovery. However, challenges remain, including limited data diversity and a lack of model interpretability. Future efforts should focus on developing robust, generalizable, and clinically interpretable models by integrating diverse and longitudinal data to enable personalized diagnosis and treatment.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.