Artificial intelligence for dementia research methods optimization
Author(s)
Type
Journal Article
Abstract
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.
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.
Date Issued
2023-12
Date Acceptance
2023-07-23
Citation
Alzheimer's and Dementia, 2023, 19 (12), pp.5934-5951
ISSN
1552-5260
Publisher
Wiley Open Access
Start Page
5934
End Page
5951
Journal / Book Title
Alzheimer's and Dementia
Volume
19
Issue
12
Copyright Statement
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Identifier
https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/alz.13441
Subjects
ALGORITHMS
ALZHEIMERS-DISEASE
artificial intelligence
BEHAVIORAL VARIANT
classification
CLASSIFICATION
Clinical Neurology
clinical utility
deep learning
dementia
DIAGNOSIS
FRONTOTEMPORAL DEMENTIA
generalizability
IDENTIFICATION
INDIVIDUALS
interpretability
Life Sciences & Biomedicine
machine learning
methods optimization
MODEL
Neurosciences & Neurology
PREDICTION
regression
replicability
Science & Technology
semi-supervised learning
supervised learning
transferability
unsupervised learning
Publication Status
Published
Date Publish Online
2023-08-28