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40/100

✓ Peer-reviewed alzhub usefulness score 40 out of 100. Digital health tools

Topic: Risk, prevention and care in population studies

A total of 2056 papers related to machine learning in patients with MCI were retrieved from the Wo SCC database. The number of papers is increasing year by year. These papers are mainly from 9,577 organizations in 498 countries, most of which are from the United States and China. The journal with the largest number of publications is the FRONTIERS IN AGING NEUROSCIENCE. Folstein M is an authoritative author from the Johns Hopkins University School of Medicine. His paper "Mini-mental state: A practical method for grading the cognitive state of patients for the clinician" is the most cited article in this field. Literature and keyword analysis indicate that MCI prediction, automated monitoring of MCI, continuous evaluation and remote monitoring of cognitive function in individuals with MCI, and interdisciplinary data integration and personalized medicine are current research hotspots and development directions. This study is the first to use bibliometric methods to visualize and analyze the application field of machine learning in MCI, revealing research trends and frontiers in this field. This information will provide a useful reference for researchers focusing on machine learning applications in the field of MCI.

Key findings

A total of 2056 papers related to machine learning in patients with MCI were retrieved from the Wo SCC database.
The number of papers is increasing year by year.
These papers are mainly from 9,577 organizations in 498 countries, most of which are from the United States and China.
The journal with the largest number of publications is the FRONTIERS IN AGING NEUROSCIENCE.
Folstein M is an authoritative author from the Johns Hopkins University School of Medicine.

Why it matters

This bibliometric analysis of 2056 papers describes research trends in machine learning for mild cognitive impairment, including prediction, automated monitoring and personalized medicine as current hotspots. It maps the literature and does not test any treatment, cure or prevention.

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Study details

Participants2,056
Evidence strengthLow

How this study scored 40/100

Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in Neurology)
Journal's citation record	2 / 4	Frontiers in Neurology: journal h-index 162 (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	3 / 4	2,056 participants
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 4 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	1.3 citations a year over 3 year(s) since 2025
Impact for its field and year	2 / 6	Cited 2.5× 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 4 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	1 / 4	Last-listed author Renli Deng: h-index 14 (OpenAlex, whole career)
Institution's track record	0 / 3	Zunyi Medical University: institution h-index 178 (OpenAlex)
Multi-centre collaboration	0 / 3	OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete)
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40470497)
Free to read	2 / 2	Free to read
Total	40 / 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).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Liu, H., Huo, Q., Li, F., Luo, X., & Deng, R. (2025). Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024. Frontiers in Neurology. https://doi.org/10.3389/fneur.2025.1587441

Vancouver

Liu H, Huo Q, Li F, Luo X, Deng R. Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024. Frontiers in Neurology. 2025. doi:10.3389/fneur.2025.1587441. PMID: 40470497.

BibTeX

@article{liu2025-40470497,
  author  = {Liu, Huan and Huo, Qing and Li, Feng and Luo, Xu and Deng, Renli},
  title   = {{Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024}},
  journal = {Frontiers in Neurology},
  year    = {2025},
  doi     = {10.3389/fneur.2025.1587441},
  pmid    = {40470497},
  url     = {https://doi.org/10.3389/fneur.2025.1587441}
}

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