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AI-enabled cardiovascular devices are increasingly used in imaging, physiological signal analysis, and clinical decision support systems. Despite growing clinical adoption, requirements for evidence generation, software change management, and post-deployment assurance remain fragmented across jurisdictions and are often difficult to translate into operational processes within healthcare organizations. This review synthesizes common foundations of software as a medical device (SaMD) oversight and compares key regulatory expectations regarding cardiovascular AI across the United States, the European Union, and the United Kingdom, with emphasis on the entire clinical lifecycle from pre-deployment assessment to post-market monitoring. Across jurisdictions, convergent operational requirements emerge: First, external validation reflecting real-world heterogeneity with assessment beyond discrimination, including calibration and clinically relevant threshold performance. Second, structured governance of software updates with predefined limits and verification/validation activities. Third, transparency and traceability documentation supporting safe use, accountability, and auditability. Finally, continuous post-market surveillance with longitudinal performance monitoring across clinically relevant subgroups. These requirements are translated into a set of practical implementation artifacts, including a transparency document template, a site acceptance testing protocol, a governance workbook aligned with predefined change planning concepts, a monitoring dashboard specification linking key performance indicators to predefined actions, and an accountability framework outlining organizational responsibilities. Representative cardiovascular use cases (CT-based functional assessment, ECG-based screening and triage, and echocardiographic quantification) illustrate how modality-specific sources of variability impact monitoring priorities and governance considerations. This synthesis supports procurement, governance, and quality assurance activities for AI-enabled cardiovascular devices while maintaining alignment with contemporary methodological standards and regulatory expectations.