ArticleDigital health
Emerging trends in Alzheimer's disease diagnosis and prediction using artificial intelligence: A bibliometric analysis of the top cited 100 articles.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Unraveling the carcinogenic mechanisms of benzo[a]pyrene in prostate cancer: a multi-omics approach.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Article
- Mapping knowledge landscapes and emerging trends in artificial intelligence in glioblastoma: A bibliometric analysis.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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 in RStudio, visualizing the results through graphical representations. Results: It was found that the top cited 100 articles came from 51 journals and 31 countries. The journal with both the highest number of published articles and the greatest citation frequency was NEUROIMAGE, while PROTEIN ENGINEERING DESIGN & SELECTION boasted the highest average citation rate. The country with the largest volume of published articles was the United States, followed by China and the United Kingdom. In terms of institutions, the University of North Carolina had produced the most publications. The keywords identified fall into 9 categories. The most frequently occurring keywords are "Alzheimers disease", "mild cognitive impairment", "classification", "MRI", "deep learning", "diagnosis", "dementia", "biomarkers", "brain atrophy", "machine learning", "voxel based morphometry", "prediction", and "patterns". Conclusion: AI-based technologies for AD diagnosis and prediction are becoming indispensable clinical tools. Future research should leverage AI to identify novel AD biomarkers, enabling precision diagnosis and treatment. However, our bibliometric analysis has limitations: language and citation biases may skew interpretation of emerging AI-AD trends.
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.