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Updated: May 24, 2026

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
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Published on: January 24, 2020

Toward Gender-Aware Digital Twins for Modeling Cognitive Decline.

Shrutha Morthala1, Ankica Babic1,2

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

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Digital twins (DTs) modeling cognitive decline must account for sex and gender differences. Incorporating these factors improves diagnostic accuracy and model performance for equitable health trajectories.

Keywords:
Digital twinscognitive declinegender-aware modeling

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Related Experiment Videos

Last Updated: May 24, 2026

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing
06:58

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

Published on: January 24, 2020

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Digital health
  • Computational neuroscience
  • Biomedical modeling

Background:

  • Digital twins (DTs) are emerging tools for modeling health trajectories.
  • Current DT frameworks often overlook sex and gender differences, potentially impacting health equity.
  • Cognitive decline is a complex condition with known sex- and gender-related variations.

Purpose of the Study:

  • To synthesize evidence on sex- and gender-related differences pertinent to digital twin modeling of cognitive decline.
  • To highlight the implications of these differences for DT accuracy and performance.
  • To advocate for gender-aware DT design and interpretation.

Main Methods:

  • Systematic review and synthesis of existing literature on sex/gender differences in cognitive decline.
  • Analysis of how these differences affect biological risk factors and disease progression.
  • Evaluation of the impact on diagnostic accuracy and computational model performance.

Main Results:

  • Significant sex- and gender-related differences exist in cognitive performance and disease progression.
  • Biological risk factors for cognitive decline show sex- and gender-specific patterns.
  • These disparities critically affect the accuracy and performance of current gender-neutral DT models.

Conclusions:

  • Gender-aware digital twin designs are essential for accurate cognitive decline modeling.
  • Transparent and equitable interpretation of model-derived information is crucial.
  • Addressing sex and gender in DTs is vital for advancing personalized and equitable healthcare.