Beyond the gender data gap: co-creating equitable digital patient twins

Digital patient twins constitute a transformative innovation in personalized
medicine, integrating patient-specific data into predictive models that leverage
artificial intelligence (AI) to optimize diagnostics and treatments. However,
existing digital patient twins often fail to incorporate gender-sensitive and
socio-economic factors, reinforcing biases and diminishing their clinical
effectiveness. This (gender) data gap, long recognized as a fundamental
problem in digital health, translates into significant disparities in healthcare
outcomes. This mini-review explores the interdisciplinary connections of
technical foundations, medical relevance, as well as social and ethical
challenges of digital patient twins, emphasizing the necessity of gender-
sensitive design and co-creation approaches. We argue that without
intersectional and inclusive frameworks, digital patient twins risk perpetuating
existing inequalities rather than mitigating them. By addressing the interplay
between gender, AI-driven decision-making and health equity, this mini-
review highlights strategies for designing more inclusive and ethically
responsible digital patient twins to further interdisciplinary approaches.