Harnessing the potential of machine learning and artificial intelligence for dementia research

Janice M. Ranson, Magda Bucholc, Donald Lyall, Danielle Newby, Laura Winchester, Neil Oxtoby, Michele Veldsman, Timothy Rittman, Sarah Marzi, Nathan Skene, Ahmad Al Khleifat, Isabelle F Foote, Vasiliki Orgeta, Andrey Kormilitzin, Ilianna Lourida, David J Llewellyn

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
58 Downloads (Pure)


Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal datasets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal datasets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.
Original languageEnglish
Article number6
Pages (from-to)1-12
Number of pages12
JournalBrain Informatics
Issue number1
Early online date24 Feb 2023
Publication statusPublished online - 24 Feb 2023

Bibliographical note

Funding Information:
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. JMR is supported by Alzheimer’s Research UK and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). DJL is supported by Alzheimer’s Research UK, National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South West Peninsula, National Health and Medical Research Council (NHMRC), National Institute on Aging/National Institutes of Health (RF1AG055654), and the Alan Turing Institute/Engineering and Physical Sciences Research Council (EP/N510129/1). NO is a UKRI Future Leaders Fellow (MR/S03546X/1) and acknowledges funding from the National Institute for Health Research University College London Hospitals Biomedical Research Centre. AAK is funded by ALS Association Milton Safenowitz Research Fellowship, The Motor Neurone Disease Association (MNDA) Fellowship (Al Khleifat/Oct21/975–799) and The NIHR Maudsley Biomedical Research Centre. TR is supported by the Cambridge Centre for Parkinson's Plus Disorders and the Cambridge Biomedical Research Centre. This work was additionally supported by the following funding: Alzheimer’s Research UK and Dr George Moore Endowment for Data Science at Ulster University (MB), George Henry Woolfe Legacy Fund (IFF).

Funding Information:
All authors are members of the Leadership Team for the non-profit Deep Dementia Phenotyping (DEMON) Network. DL and JR are founders of the DEMON Network, the Alzheimer’s Association ISTAART Professional Interest Area in AI for Precision Dementia Medicine, and the Alan Turing Institute Interest Group in Precision Dementia Medicine. With thanks to collaborators from the 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.

Funding Information:
AK declares research grant funding from GlaxoSmithKline. All other authors declare no competing interests.

Publisher Copyright:
© 2023, The Author(s).


  • dementia
  • artificial intelligence
  • machine learning
  • genetics
  • drug discovery
  • neuroimaging
  • prevention
  • iPSC
  • animal models
  • Neuroimaging
  • Animal models
  • Prevention
  • Machine learning
  • Genetics
  • Drug discovery
  • Artificial intelligence
  • Dementia


Dive into the research topics of 'Harnessing the potential of machine learning and artificial intelligence for dementia research'. Together they form a unique fingerprint.

Cite this