Predictive Analysis and Review of Cardiovascular Diseases in Women Using Artificial Intelligence with Clinical and Ethical Implementation Issues

Abstract

Cardiovascular Diseases (CVDs) are the leading global cause of death and affect women disproportionately due to sex-specific risk factors, atypical symptoms, and frequent underdiagnosis. Despite advances in AI and ML that improve prediction and diagnosis, most models are male-biased, lowering accuracy for women. This paper presents the review of AI/ML Techniques for CVD Detection Techniques including the Deep learning methods, including CNN–LSTM and transformer architectures enhance ECG analysis and plaque detection but face issues with data imbalance, heterogeneity, and explainability. Bridging these gaps requires sex-balanced datasets, fairness-aware algorithms, and inclusion of female biomarkers. Future research should focus on sex-stratified validation, transparent reporting frameworks like TRIPOD-AI and CONSORT-AI, and prospective clinical evaluation in women with clinical and ethical implementation issues.

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