A critical disparity in cardiovascular research threatens the equitable application of emerging artificial intelligence technologies. Analysis of major cardiac trials spanning 1990 to 2020 reveals that approximately 70 percent of cardiovascular research subjects were male, while women comprised only 30 percent of study populations. This historical imbalance creates a significant challenge for AI-powered digital twin systems, which depend on representative datasets to generate accurate predictions across diverse patient populations.
Digital twin technology requires robust, sex-stratified data to account for documented biological differences in women’s cardiac physiology, including distinct symptom presentations, anatomical variations, and hormonal factors. Without adequate female representation in training datasets, algorithms risk producing less accurate models for women, potentially undermining the precision medicine promise these technologies offer.
Medical experts and regulatory agencies now recognize that addressing these historical data gaps is essential to ensuring digital twins deliver equitable clinical benefits across all patient populations.
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