Artificial intelligence for dementia research methods optimization

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.

Artificial intelligence for neurodegenerative experimental models

“…A more comprehensive overview of ML/AI methodology can be found in the methods optimization paper from the same series. 32 Mathematical and statistical modeling of experimental models based on prior domain knowledge (eg, structural equation modeling…”

“…Here we highlight several approaches that have been used to enhance reproducibility and translation, including those that have yet to be adopted in dementia research. A more comprehensive overview of ML/AI methodology can be found in the methods optimization paper from the same series 32 . Mathematical and statistical modeling of experimental models based on prior domain knowledge (eg, structural equation modeling [SEM]) is an approach that can be used to support hypothesis generation and testing, provide insight into biological mechanisms, and predict the effects of interventions 150 .…”

“…Together, this series provides a comprehensive overview of current applications of AI to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models (this article), drug discovery and trials optimization, 26 genetics and omics, 27 biomarkers, 28 neuroimaging, 29 prevention, 30 applied models and digital health, 31 and methods optimization 32 …”

Artificial intelligence for dementia prevention

“…Semi‐supervised methods use information from unlabeled data to improve the performance of models trained on labeled data 37,38 . A more in‐depth description of ML methodologies and applications to the broader dementia field can be found in the methods optimization paper also included in this special issue 39 …”

“…Together, this series provides a comprehensive overview of current applications of AI and ML to dementia, and future opportunities for innovation to accelerate research. Each review focuses on a different area of dementia research, including experimental models, 46 drug discovery and trials optimization, 47 genetics and omics, 48 biomarkers, 49 imaging, 50 prevention (this article), applied models and digital health, 51 and methods optimization 39 …”