Toward demographic robustness in digital patient twins: Addressing the gender data gap

In this opinion piece, we argue that sex- and gender-based equity must become a foundational criterion in the design and implementation of digital patient twins. Digital patient twins offer a promising avenue for precision medicine by simulating individual health states and treatment responses. However, their clinical utility and fairness depend on whether diverse patient populations are adequately represented and accounted for in the data and devices on which these models are built. Drawing on evidence from cardiology, endocrinology, mental health, and medical device research, this article shows how current digital patient twin initiatives often overrepresent male, white, and socioeconomically privileged populations, while women, gender-diverse individuals, and people of color remain underrepresented. These imbalances can lead to systematic misdiagnoses, misinterpretation of physiological variation, and measurement inaccuracies. Documented examples include under-recognition of heart failure with preserved ejection fraction in women, omission of menstrual cycle-related changes in glycemic control, underdiagnosis of depression in women by speech-based AI models, and oxygen saturation overestimation in patients with darker skin tones. We argue that these disparities are rooted in structural biases in clinical research and are perpetuated when sex- and gender-specific variables, intersectional factors, and subgroup validation are absent from model design. Addressing these limitations requires balanced data representation, integration of sex- and gender-informed knowledge, participatory design with diverse patient groups, subgroup performance testing, transparent reporting, and mitigation of device-related bias. We contend that these are not optional refinements but prerequisites for realizing the promise of personalized care without reproducing or deepening existing health inequities.