Artificial intelligence for dementia prevention.

Area of Science:

• Neuroscience
• Computational Biology
• Public Health

Background:

• Dementia prevention research has identified numerous modifiable risk factors, but their interactions and causal roles require further investigation.
• Current methods for identifying high-risk individuals for clinical trials and drug development are suboptimal.
• Artificial intelligence (AI) and machine learning (ML) offer potential solutions to refine understanding and application of dementia risk factors.

Purpose of the Study:

• To explore the application of machine learning (ML) approaches in dementia prevention.
• To evaluate the current uses, limitations, and future potential of ML in dementia prevention strategies.
• To highlight how AI can enhance precision in dementia risk management and clinical trial design.

Main Methods:

• Review and discussion of exemplar machine learning (ML) applications in dementia prevention research.
• Evaluation of the performance and limitations of current ML-based risk-profiling and trial-recruitment tools.
• Analysis of the potential of AI and ML in drug repurposing and prioritizing therapeutics.

Main Results:

• ML models show potential for developing improved risk-profiling and clinical trial recruitment tools, potentially reducing costs and enhancing trial efficiency.
• ML can inform drug repurposing and aid in prioritizing disease-modifying therapeutics for dementia.
• Current ML applications in dementia prevention are limited, but the technology holds significant promise for future advancements.

Conclusions:

• Machine learning (ML) is poised to significantly enhance precision in dementia prevention, though it is not yet widely adopted.
• AI can personalize risk-management tools and target specific patient groups for clinical trials, improving dementia prevention outcomes.
• Further research is needed to gain causal insights into dementia risk factors across the lifespan to fully leverage AI in prevention efforts.