Artificial Intelligence for Dementia Research Methods Optimization

Magda Bucholc* (Corresponding Author), 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

*Corresponding author for this work

Research output: Working paper


INTRODUCTION: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.

METHODS: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research.

RESULTS: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future.

DISCUSSION: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Original languageEnglish
Publication statusSubmitted - 2 Mar 2023


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


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