Sex, gender and artificial intelligence in cardiovascular medicine 4.0

Cardiovascular disease is the leading cause of mortality in women worldwide, yet the clinical and algorithmic infrastructure of cardiovascular medicine was constructed around a male prototype. The convergence of artificial intelligence, big data, wearable technologies and telemedicine, collectively termed Medicine 4.0, offers transformative potential for sex- and gender-specific cardiology. However, these tools risk perpetuating and algorithmically entrenching the sex and gender biases embedded in historical clinical datasets. This paper examines the mechanistic underpinnings of algorithmic sex/gender bias in cardiovascular artificial intelligence across six interacting bias categories, analyzes the epistemic risks of binary sex stratification in machine learning and proposes a structured operational framework comprising gender-aware clinical prompt engineering, a three-phase model for responsible artificial intelligence interaction and a coordinated agenda spanning data governance, algorithmic design, clinical education and regulatory oversight. Grounded in the Lancet Commission on Gender and Global Health's framing of gender distortion in health systems as a driver of structural injustice, this framework argues that precision cardiovascular medicine is scientifically meaningful only when it is equitable.