METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.
RESULTS: Risk‐profiling tools may help identify high‐risk populations for clinical trials; however, their performance needs improvement. New risk‐profiling and trial‐recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug‐repurposing efforts and prioritization of disease‐modifying therapeutics.
DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.
Highlights: Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk‐profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk‐management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.
Bibliographical noteFunding Information:
With thanks to the Deep Dementia Phenotyping (DEMON) Network State of the Science symposium participants (in alphabetical order): Peter Bagshaw, Robin Borchert, Magda Bucholc, James Duce, Charlotte James, David Llewellyn, Donald Lyall, Sarah Marzi, Danielle Newby, Neil Oxtoby, Janice Ranson, Tim Rittman, Nathan Skene, Eugene Tang, Michele Veldsman, Laura Winchester, and Zhi Yao. This review was facilitated by the Alzheimer's Association International Society to Advance Alzheimer's research and Treatment (ISTAART), through the AI for Precision Dementia Medicine Professional Interest Area (PIA). The views and opinions expressed in this publication represent those of the authors and do not necessarily reflect those of the PIA membership, ISTAART, or the Alzheimer's Association. This article was the product of a DEMON Network State of the Science symposium entitled “Harnessing Data Science and AI in Dementia Research” funded by Alzheimer's Research UK. J.M.R. and D.J.L. are supported by Alzheimer's Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). D.J.L. also receives funding from the Medical Research Council (MR/X005674/1), National Institute for Health Research (NIHR) Applied Research Collaboration South West Peninsula, National Health and Medical Research Council (NHMRC), and National Institute on Aging/National Institutes of Health (RF1AG055654). This work was additionally supported by the following: European Research Council (grant agreement no. 803239 (A.K.L.), Barts Charity (C.R.M.), George Henry Woolfe Legacy Fund and the National Institute on Aging (RF1AG073593) (I.F.F.), E.Y.H.T. (National Institute for Health Research (NIHR) Clinical Lecturer) is funded by the NIHR and the views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS, or the UK Department of Health and Social Care. S.B. is supported by Dementias Platform UK (DPUK). The Medical Research Council supports DPUK through grant MR/T0333771. M.B. is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (Special EU Programmes body (SEUPB)) INTERREG (European Region Development Fund (ERDF)/SEUPB), Health and Social Care Research and Development HSC R&D (COM/5750/23) and Dr George Moore Endowment for Data Science at Ulster University.
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
- machine learning
- artificial intelligence
- risk prediction