Evidence map›Paper›PMID 37496259›Full record

ReviewAlzheimer's & dementia : the journal of the Alzheimer's Association2023

Artificial intelligence for dementia-Applied models and digital health.

Donald M Lyall, Andrey Kormilitzin, Claire Lancaster, Jose Sousa, Fanny Petermann-Rocha, Christopher Buckley, Eric L Harshfield, Matthew H Iveson, Christopher R Madan, Ríona McArdle and 9 more

Open access · hybridAbstract readReview
In one paragraph

Review in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
13.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 62 citations in OpenAlex.

  1. Digital Therapeutics in China: Comprehensive Review.Journal of medical Internet research · 2025
    Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. Review
  6. Article
  7. Detecting Alzheimer's disease using digital virtual reality cognitive tests.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  8. Article
  9. Bridging Gaps in Sundown Syndrome Research: a Scoping Review and Roadmap for Future Multimodal Approaches.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2025
    Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Detection of emergency department patients at risk of dementia through artificial intelligence.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Observational
  18. Review
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors at 13 institutions in 4 countries.

Donald M LyallSchool of Health and Wellbeing, College of Medical and Veterinary Sciences, University of Glasgow, Glasgow, UK.
Andrey KormilitzinDepartment of Psychiatry, University of Oxford, Oxford, UK.
Claire LancasterSchool of Psychology, University of Sussex, Brighton, UK.
Jose SousaPersonal Health Data Science, SANO-Centre for Computational Personalised Medicine, Krakow, Poland.
Fanny Petermann-RochaSchool of Health and Wellbeing, College of Medical and Veterinary Sciences, University of Glasgow, Glasgow, UK.
Christopher BuckleyDepartment of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, UK.
Eric L HarshfieldStroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
Matthew H IvesonCentre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Christopher R MadanSchool of Psychology, University of Nottingham, Nottingham, UK.
Ríona McArdleTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
Danielle NewbyDepartment of Psychiatry, University of Oxford, Oxford, UK.
Vasiliki OrgetaDivision of Psychiatry, University College London, London, UK.
Eugene TangTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
Stefano TamburinDepartment of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
Lokendra S ThakurBroad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
Ilianna LouridaUniversity of Exeter Medical School, Exeter, UK.
Deep Dementia Phenotyping (DEMON) NetworkUniversity of Exeter Medical School, Exeter, UK.
David J LlewellynUniversity of Exeter Medical School, Exeter, UK.
Janice M RansonUniversity of Exeter Medical School, Exeter, UK.
University of Exeter · GBNewcastle University · GBUniversity of Glasgow · GBUniversity of Oxford · GBBroad Institute · USNorthumbria University · GBQueen's University Belfast · GBUniversity College London · GBUniversity of Cambridge · GBUniversity of Edinburgh · GBUniversity of Nottingham · GBUniversity of Sussex · GBUniversity of Verona · IT

Funding

Identifying modifiable aspects of gene-by-environment interplay in later-life cognitive declineRF1AG055654 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI FAUL, JESSICA, GALAMA, TITUS JOHANNES · 2017 to 2018
$4.3M
Medical Research Council MR/X005674/1NIA NIH HHS RF1 AG055654
6 · The paper itself

Abstract

introductionThe use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as "deep phenotyping" cohorts with multi-omics health data become available.

methodsThis narrative review synthesizes understanding of applied models and digital health technologies, in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show evidence of improved predictive power compared to standard clinical risk scores in predicting dementia, and the potential to decompose large numbers of variables into relatively few critical predictors.

resultsThis review focuses on key areas of emerging promise including: emphasis on easier, more transparent data sharing and cohort access; integration of high-throughput biomarker and electronic health record data into modeling; and progressing beyond the primary prediction of dementia to secondary outcomes, for example, treatment response and physical health. DISCUSSION: Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g., online), or naturalistic (e.g., watch-based accelerometry).

Indexed as

Artificial IntelligenceDementiaDigital HealthHumansMachine LearningAIapplied modelsartificial intelligencedementiadigital healthmachine learningML

Identifiers

PMID37496259
PMCPMC10955778
OpenAlexW4385294584

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.