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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
Summary
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.
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.
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