Artificial intelligence for dementia research methods optimization.

Area of Science:

• Computational neuroscience
• Medical informatics
• Geriatric medicine

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly applied in dementia research.
• Methodological challenges limit insights from high-dimensional data and clinical translation.
• Reproducibility and generalizability are key concerns in AI/ML for dementia.

Purpose of the Study:

• To identify and address methodological challenges in applying AI and ML to dementia research.
• To enhance the potential of AI and ML for improving dementia diagnosis, prevention, and management.
• To promote best practices for robust, generalizable, and interpretable AI/ML models in dementia care.

Main Methods:

• Review and synthesis of current challenges in AI/ML application for dementia.
• Emphasis on open-source code, data sharing, and interpretable modeling.
• Advocacy for diverse datasets and adherence to reporting guidelines.

Main Results:

• Inadequate reporting of ML procedures impedes result reproduction and replication.
• Models trained on unrepresentative datasets exhibit poor generalizability.
• Lack of defined metrics and concerns about interpretability hinder clinical adoption.

Conclusions:

• Overcoming methodological challenges in AI/ML is essential for advancing dementia research and care.
• Prioritizing code/data sharing, interpretability, and diverse datasets will improve model robustness and reduce bias.
• Standardized reporting guidelines are needed for clarity and reproducibility in AI-driven dementia studies.