A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.
ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.
Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.
ML is not yet widely used but has considerable potential to enhance precision in dementia prevention.
Artificial intelligence (AI) is not widely used in the dementia prevention field.
Risk-profiling tools are not used in clinical practice.
Causal insights are needed to understand risk factors over the lifespan.
AI will help personalize risk-management tools for dementia prevention.
AI could target specific patient groups that will benefit most for clinical trials.
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This post is Copyright: Danielle Newby,
Charles R. Marshall,
Christopher P. Albertyn,
Isabelle F. Foote,
Anja K. Leist,
Eugene Y. H. Tang,
Xin You Tai,
The Deep Dementia Phenotyping (DEMON) Network,
David J. Llewellyn,
Janice M. Ranson | October 14, 2023