27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.
Early diagnosis of cognitive disorders in older adults is a major healthcare priority with benefits to patients, families, and health systems. 1 key finding →
Design: systematic review Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
Supervised intervention delivery is linked to greatest benefits. 2 key findings →
Design: meta-analysis Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. 2 key findings →
Design: systematic review Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. 1 key finding →
Design: cohort study Participants: 37,096 Evidence: moderate
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Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. 1 key finding →
Evidence: low
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The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging. 2 key findings →
Design: systematic review Evidence: high
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Both interventions were highly feasible and acceptable and allowed improvement in different aspects of cognitive and psychosocial functioning in MCI subjects. 4 key findings →
Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
The study consists of 25,186 patients (mean age = 63.0, interquartile range [IQR] of age = 15.1 years, male = 50%). 3 key findings →
Design: cohort study Participants: 25,186 Evidence: moderate
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The testing conditions and the accessibility of telemedicine yield inconclusive results as to whether telemedicine can improve the management of dementia in geriatric individuals. 4 key findings →
Design: systematic review Evidence: high
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To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. 5 key findings →
Design: longitudinal study Participants: 473 Evidence: moderate
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NULISA plasma biomarkers revealed disease-specific protein patterns. 5 key findings →
Design: systematic review Participants: 194 Evidence: high
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Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Digital cognitive tests showed good performances for MCI and dementia. 1 key finding →
Participants: 56 Evidence: low
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Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future. 2 key findings →
Design: meta-analysis Evidence: high
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There is increasing interest in the use of technology to support social health in dementia. 5 key findings →
Design: systematic review Evidence: high
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This simple and feasible cognitive impairment prediction model could identify community-dwelling elderly people at the greatest 3-year risk for cognitive impairment, which could help community nurses in the early identification of dementia. 1 key finding →
Design: prospective cohort Evidence: moderate
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Digital RT proved an innovative approach to manage PWD and will thus help PWD achieve a better mood and have more opportunities to engage in social interactions. 3 key findings →
Participants: 25 Evidence: low
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The dementia population is increasing as the world's population is growing older. 1 key finding →
Design: systematic review Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
This analysis provides a comprehensive overview of the AI-related research conducted in the field of health care, which helps researchers, policy makers, and practitioners better understand the development of health care-related AI research and possible practice implications. 2 key findings →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals' history of health and healthcare beyond existing risk prediction models. 1 key finding →
Participants: 40,736 Evidence: moderate
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Surprisingly, fallers and non-fallers did not differ on any conventional assessment (p = 0.069-0.991), except for 'previous faller' (p = 0.006). 5 key findings →
Design: cohort study Participants: 77 Evidence: moderate
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Unsupervised machine learning identifies high likelihood of dementia in population-based surveys, even without cognitive and behavioral measures and without the need of clinical diagnosis of dementia in a subsample of the population. 2 key findings →
Participants: 18,165 Evidence: low
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DCR outperforms the MMSE in detecting and classifying cognitive impairment-in a fraction of the time-while being not influenced by a patient's ethnicity. 5 key findings →
Participants: 360 Evidence: low
Full summary, score breakdown and related studies → Original source Free full text
Between 2011 and 2014, seniors experiencing health decline used various digital health technologies at low absolute rates (range: 1%-20%). 2 key findings →
Design: longitudinal study Participants: 4,037 Evidence: moderate
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Among 2699 articles initially screened, 21 studies were included, encompassing over 1 million participants. 5 key findings →
Design: systematic review Evidence: high
Full summary, score breakdown and related studies → Original source Free full text
Free full text links and the Full text may need a subscription label come from Unpaywall, Europe PMC and OpenAlex records of free copies, checked 10 Oct 2026. A study with neither is unknown, not paywalled. Show only studies that are free to read
This is alzhub's own score. It is not an official metric, not an impact factor, and no journal, publisher or ranking body endorses it. We built it so that a reader who is not a scientist can tell a landmark trial from a conference abstract without having to already know the difference. Every point comes from one of 20 components, all of them printed on the study itself, so you can disagree with our arithmetic rather than having to trust the total.
Journals are placed in ordered tiers, never given a numeric impact factor — we could not verify current impact factors from a source we control, and an invented one would be worse than none. Tier 1 is the top-tier general journals (NEJM, The Lancet, JAMA, Nature, Science, Nature Medicine); tier 2 is the leading specialty journals; tier 3 is every other peer-reviewed journal. A separate part scores how much the journal's own papers are cited, which tells the tier-3 journals apart.
Two rules we don't bend. First, we never fill in a missing value: if a record does not state a sample size or a design, that component scores zero, and if we simply have no data for a part, such as a citation count, that part is left out and the study is scored on the parts we could check. The study card says which. Second, anything that is not a peer-reviewed article is capped at 16/100 and carries a warning chip — so a conference abstract or a company press release can never outrank a peer-reviewed paper, no matter how good it looks.
We rank a rigorous null result — a good trial showing that something does not work — above an early positive finding. Someone deciding whether to buy a supplement is served by that, and the rigour is already counted by the design and journal components.
The score is computed from the published data alone and is reproducible: the same file always yields the same numbers.
See the whole rubric drawn out → — four charts covering all 27,380 studies: how the scores are spread and where the cap bites, which of the 20 components the library is strong and weak on, which subjects rest on stronger evidence, and what kinds of study each subject is actually built from.
This is a large sample of the Alzheimer's literature, not an exhaustive index of it. It holds 27,380 records, built mostly from automated pulls of Europe PMC (60.6% of the library) and OpenAlex (18.6%), plus smaller pulls from PubMed, Crossref and medRxiv. If a study, a drug, or a finding isn't here, that is not evidence it doesn't exist in the literature — it may simply not have been pulled in yet.
1,032 records (3.8% of the library) are not peer-reviewed — 962 of those are preprints posted before any journal review, and the rest are conference abstracts, regulatory notices and reports. Every study card on this page states its status plainly, and the score above is capped for anything that isn't a peer-reviewed article.
See the full, machine-readable breakdown of this library's coverage →