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Artificial Intelligence for Dementia - Applied Models and Digital Health

Lookup NU author(s): Claire Lancaster, Dr Chris Buckley, Dr Ríona McArdle, Dr Eugene TangORCiD, Dr Ilianna Lourida



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


The use of applied modelling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefit, particularly as ‘deep phenotyping’ cohorts with multi-omics health data become available. This 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. This review focusses 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 modelling, and progressing beyond primary prediction of dementia to secondary outcomes e.g., treatment response and physical health. Such approaches will benefit also from improvements in remote data measurement, whether cognitive (e.g. online), or naturalistic (e.g. watch-based accelerometry).

Publication metadata

Author(s): Lyall D, Kormilitzin A, Lancaster C, Sousa J, Petermann-Rocha F, Buckley C, Harshfield E, Iveson M, Madan C, Mc Ardle R, Newby D, Orgeta V, Tang E, Tamburin S, Thakur L, Lourida I, Deep Dementia Phenotyping Network The, Llewellyn D, Ranson J

Publication type: Review

Publication status: Published

Journal: Alzheimer's & Dementia: The Journal of the Alzheimer's Association

Year: 2023

Volume: 19

Issue: 12

Pages: 5872-5884

Print publication date: 01/12/2023

Online publication date: 26/07/2023

Acceptance date: 26/05/2023

ISSN (electronic): 1552-5279


DOI: 10.1002/alz.13391