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.

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.

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This post is Copyright: 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,
The Deep Dementia Phenotyping (DEMON) Network,
Ilianna Lourida,
David J. Llewellyn,
Janice M. Ranson | August 29, 2023

Wiley: Alzheimer’s & Dementia: Table of Contents