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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Designing a Multimodal Data Structure for Cognitive Decline Using All of Us Resources and Gemini Flash Thinking.

Oliwia Kudyba1, Ankica Babic1,2

  • 1Department of Information Science and Media Studies, University of Bergen, Norway.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

This study introduces a new AI framework to understand cognitive decline by integrating diverse data and enhancing model interpretability. It aims to move beyond correlation to explore causality in medical AI.

Keywords:
All of UsCognitive declineGemini Flash Thinkingcausality modelingstudy design

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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.