27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.
Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. 5 key findings →
Design: systematic review Evidence: high
Full summary, score breakdown and related studies → Original source
Explainable artificial intelligence (XAI) has gained much interest in recent years for its ability to explain the complex decision-making process of machine learning (ML) and deep learning (DL) models. 1 key finding →
Design: systematic review Evidence: high
Full summary, score breakdown and related studies → Original source
Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. 2 key findings →
Design: meta-analysis Evidence: high
Full summary, score breakdown and related studies → Original source
For effective treatment of Alzheimer's disease (AD), it is important to identify subjects who are most likely to exhibit rapid cognitive decline. 3 key findings →
Design: longitudinal study Participants: 139 Evidence: moderate
Full summary, score breakdown and related studies → Original source
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
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
Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Brain aging is accompanied by patterns of functional and structural change. 1 key finding →
Design: longitudinal study Evidence: moderate
Full summary, score breakdown and related studies → Original source
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. 2 key findings →
Participants: 1,608,741 Evidence: moderate
Full summary, score breakdown and related studies → Original source
Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. 5 key findings →
Participants: 2,739 Evidence: moderate
Full summary, score breakdown and related studies → Original source
When validated with subjects diagnosed using an official test, the model achieves COVID-19 sensitivity of 98.5% with a specificity of 94.2% (AUC: 0.97). 5 key findings →
Participants: 19 Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. 1 key finding →
Participants: 647 Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. 1 key finding →
Participants: 339 Evidence: low
Full summary, score breakdown and related studies → Original source
These preliminary findings suggest that derived digital biomarkers may identify cognitive impairment in preclinical and prodromal AD, and may predict disease progression. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. 1 key finding →
Participants: 397 Evidence: low
Full summary, score breakdown and related studies → Original source
While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). 2 key findings →
Participants: 285 Evidence: low
Full summary, score breakdown and related studies → Original source
The use of EHRs for AD-related research is promising, and AD-BERT shows superior predictive performance in modeling MCI-to-AD progression prediction. 1 key finding →
Participants: 3,657 Evidence: low
Full summary, score breakdown and related studies → Original source
By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. 3 key findings →
Participants: 1,002 Evidence: low
Full summary, score breakdown and related studies → Original source
We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. 5 key findings →
Design: cross-sectional Evidence: low
Full summary, score breakdown and related studies → Original source
In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlated. 1 key finding →
Participants: 1,984 Evidence: low
Full summary, score breakdown and related studies → Original source
Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer's disease (AD) is the most common. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Using pre-trained language models can improve AD prediction. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD. 1 key finding →
Participants: 167 Evidence: low
Full summary, score breakdown and related studies → Original source
The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. 1 key finding →
Participants: 116 Evidence: low
Full summary, score breakdown and related studies → Original source
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 →