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Emerging trends in Alzheimer's disease diagnosis and prediction using artificial intelligence: A bibliometric analysis of the top cited 100 articles
Guangheng Zhang, Weijie Zhao, Shimeng Lv, Ziyue Wang, Yunhao Yi, Haoteng Ma, Yitong Lu, Wei Yan et al.. Digital Health, 2025-05-01
Topic: Amyloid, tau and blood-test diagnosis
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". 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.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Zhang, G., Zhao, W., Lv, S., Wang, Z., Yi, Y., Ma, H., et al. (2025). Emerging trends in Alzheimer's disease diagnosis and prediction using artificial intelligence: A bibliometric analysis of the top cited 100 articles. Digital Health. https://doi.org/10.1177/20552076251362098
Vancouver
Zhang G, Zhao W, Lv S, Wang Z, Yi Y, Ma H, et al. Emerging trends in Alzheimer's disease diagnosis and prediction using artificial intelligence: A bibliometric analysis of the top cited 100 articles. Digital Health. 2025. doi:10.1177/20552076251362098. PMID: 40688579.
BibTeX
@article{zhang2025-40688579,
author = {Zhang, Guangheng and Zhao, Weijie and Lv, Shimeng and Wang, Ziyue and Yi, Yunhao and Ma, Haoteng and Lu, Yitong and Yan, Wei and others},
title = {{Emerging trends in Alzheimer's disease diagnosis and prediction using artificial intelligence: A bibliometric analysis of the top cited 100 articles}},
journal = {Digital Health},
year = {2025},
doi = {10.1177/20552076251362098},
pmid = {40688579},
url = {https://doi.org/10.1177/20552076251362098}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
Key findings
- 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.
Why it matters
This bibliometric analysis of the top 100 cited articles describes trends in artificial intelligence for Alzheimer's diagnosis and prediction. It maps the literature and does not test any treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 34/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Digital Health) |
| Journal's citation record | 1 / 4 | Digital Health: journal h-index 77 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 3 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 1.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Jing Teng: h-index 9 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Shandong University of Traditional Chinese Medicine: institution h-index 184 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40688579) |
| Free to read | 2 / 2 | Free to read |
| Total | 34 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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