Artificial intelligence for dementia research methods optimization

, Magda Bucholc, Charlotte James, Ahmad Al Khleifat, AmanPreet Badhwar, Natasha Clarke, Amir Dehsarvi, Christopher R. Madan, Sarah J. Marzi, Cameron Shand, Brian M. Schilder, Stefano Tamburin, Hanz M. Tantiangco, Ilianna Lourida, David J. Llewellyn, Janice M. Ranson

Research output: Contribution to journalArticlepeer-review


Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
Original languageEnglish
JournalAlzheimer's and Dementia
Early online date28 Aug 2023
Publication statusPublished online - 28 Aug 2023

Bibliographical note

Funding 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, Zhi Yao. This paper 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). M.B. is supported by Alzheimer's Research UK, Economic and Social Research Council (ES/W010240/1), EU (SEUPB) INTERREG (ERDF/SEUPB), and HSC R&D (COM/5750/23). This work was additionally supported by Alzheimer's Research UK (C.J.), National Institute for Health and Care Research Bristol Biomedical Research Centre (C.J.), Fonds de recherche du Québec Santé—Chercheur boursiers Junior 1 (A.B.), Canadian Consortium for Neurodegeneration in Aging and the Courtois Foundation (A.B., N.C.), the Motor Neurone Disease Association Fellowship (Al Khleifat/Oct21/975‐799) (A.A.K.), ALS Association Milton Safenowitz Research Fellowship (22‐PDF‐609) (A.A.K.), NIHR Maudsley Biomedical Research Centre (A.A.K.), the Darby Rimmer Foundation (A.A.K.), UKRI Future Leaders Fellowship (MR/S03546X/1) (C.S.), E‐DADS project (EU JPND) (C.S.), EuroPOND project (EU Horizon 2020, no. 666992) (C.S.). S.J.M. is funded by the Edmond and Lily Safra Early Career Fellowship Program and the UK Dementia Research Institute, which receives its funding from UK DRI Ltd., funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK.

Publisher Copyright:
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.


  • machine learning
  • artificial intelligence
  • dementia
  • classification
  • regression
  • clinical utility
  • replicability
  • interpretability
  • semi‐supervised learning
  • supervised learning
  • unsupervised learning
  • methods optimization
  • deep learning
  • generalizability
  • transferability
  • semi-supervised learning


Dive into the research topics of 'Artificial intelligence for dementia research methods optimization'. Together they form a unique fingerprint.

Cite this