Designing a Multimodal Data Structure for Cognitive Decline Using All of Us Resources and Gemini Flash Thinking

Area of Science:

• Artificial Intelligence in Medicine
• Cognitive Neuroscience
• Data Science

Background:

• Cognitive decline presents complex, multilayered data challenges.
• Current AI models in medicine often suffer from the "Black Box" problem, hindering interpretability.
• Integrating heterogeneous data is crucial for a comprehensive understanding of cognitive decline.

Purpose of the Study:

• To propose a conceptual and methodological framework for enhancing the interpretability of AI models in medicine.
• To address the challenge of defining and understanding cognitive decline through integrated data.
• To move beyond correlational findings towards exploring causality in medical AI.

Main Methods:

• Development of a multilayer data structure for integrating diverse health dimensions.
• Application of the All of Us Resource for comprehensive data integration.
• Utilization of Gemini Flash Thinking to analyze data and explore causal relationships.

Main Results:

• A conceptual framework for interpretable AI in medicine has been established.
• The framework facilitates the integration of multilayered and heterogeneous data relevant to cognitive decline.
• A foundation for dynamic modeling and causal inference in medical AI has been laid.

Conclusions:

• The proposed framework enhances AI interpretability in medicine, particularly for complex conditions like cognitive decline.
• Integrating diverse datasets, such as from the All of Us Resource, is key to advancing AI in healthcare.
• Future work will focus on refining the AI structure and developing interpretable outputs for diverse populations.