This paper examines how sex and gender biases are embedded in AI-driven cardiovascular medicine (Medicine 4.0), analyzing six categories of algorithmic bias and proposing a framework for gender-aware AI design, clinical prompt engineering, and regulatory oversight.
The authors identify that historical male-prototype clinical datasets risk entrenching sex/gender bias in cardiovascular AI tools, and propose a structured operational framework—including a three-phase responsible AI interaction model and coordinated data governance, algorithmic design, education, and regulatory agenda—to address these gaps.
This is a narrative/opinion framework paper without empirical data, a systematic review protocol, or direct clinical validation of the proposed framework. No patient-level outcomes or comparative effectiveness data are reported.
When using AI tools for cardiovascular risk assessment or decision support, clinicians should actively question whether the underlying training data adequately represents female and non-binary patients. Gender-aware prompt engineering and critical appraisal of algorithmic outputs are practical first steps until equitable AI standards are formally adopted.
