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33/100

✓ Peer-reviewed alzhub usefulness score 33 out of 100. Digital health tools

Topic: Amyloid, tau and blood-test diagnosis

Conventional dementia screening tools, whether paper tests or static artificial intelligence (AI) models, capture only a snapshot of cognition and need to be adminstered periodically. Yet decline is dynamic, shaped by environment, comorbidities, and life history. Current approaches rarely adapt, integrate feedback, or provide transparent reasoning that clinicians can trust. We propose agentic AI systems: modular agents built on large language models (LLMs) that collaborate, adapt, and mirror interdisciplinary care. The Cognitive Agent Lab exemplifies this framework, emphasizing functional aspects (inputs, outputs, workflows) and non-functional aspects (transparency, adaptivity, robustness, clinical alignment). Embedding reasoning and collaboration, agentic AI offers a roadmap toward more personalized and explainable cognitive health tools.

Key findings

Yet decline is dynamic, shaped by environment, comorbidities, and life history.
Current approaches rarely adapt, integrate feedback, or provide transparent reasoning that clinicians can trust.
We propose agentic AI systems: modular agents built on large language models (LLMs) that collaborate, adapt, and mirror interdisciplinary care.
The Cognitive Agent Lab exemplifies this framework, emphasizing functional aspects (inputs, outputs, workflows) and non-functional aspects (transparency, adaptivity, robustness, clinical alignment).
Embedding reasoning and collaboration, agentic AI offers a roadmap toward more personalized and explainable cognitive health tools.

Why it matters

This paper proposes using 'agentic AI' systems built on large language models to make dementia cognitive screening more adaptive and explainable. It is a proposed roadmap, not a tested treatment, cure, or prevention for Alzheimer's disease.

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Study details

Evidence strengthLow

How this study scored 33/100

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. We have no data on impact for its field and year, so that was left out: it earned 31 of the 94 points we could judge it on, which is 33 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Journal of Alzheimer s Disease Reports)
Journal's citation record	0 / 4	Journal of Alzheimer s Disease Reports: journal h-index 40 (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 2 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.7 citations a year over 3 year(s) since 2025
Impact for its field and year	— / 6	No field-weighted citation impact (OpenAlex has none yet) — left out of this score
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	2 / 4	Last-listed author Hui Yong Yang: h-index 35 (OpenAlex, whole career)
Institution's track record	1 / 3	Pennsylvania State University: institution h-index 1,397 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41393341)
Free to read	2 / 2	Free to read
Points	31 / 94	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 31 of the 94 points we could judge it on is 33 out of 100.
Total	33 / 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

Mekulu, K., Aqlan, F., & Yang, H. Y. (2025). Agentic artificial intelligence in cognitive screening: A translational roadmap for dementia care. Journal of Alzheimer s Disease Reports. https://doi.org/10.1177/25424823251407989

Vancouver

Mekulu K, Aqlan F, Yang HY. Agentic artificial intelligence in cognitive screening: A translational roadmap for dementia care. Journal of Alzheimer s Disease Reports. 2025. doi:10.1177/25424823251407989. PMID: 41393341.

BibTeX

@article{mekulu2025-41393341,
  author  = {Mekulu, Kevin and Aqlan, Faisal and Yang, Hui Yong},
  title   = {{Agentic artificial intelligence in cognitive screening: A translational roadmap for dementia care}},
  journal = {Journal of Alzheimer s Disease Reports},
  year    = {2025},
  doi     = {10.1177/25424823251407989},
  pmid    = {41393341},
  url     = {https://doi.org/10.1177/25424823251407989}
}

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Referenced by this study and also in the alzhub library, so you can read them here.

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