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The provided documents examine the critical intersection of algorithmic fairness, regulatory compliance, and risk management within healthcare AI systems. They highlight how clinical AI bias can result from unrepresentative data or flawed model designs, ultimately threatening patient safety and health equity. To address these vulnerabilities, the texts propose structured governance frameworks and action plans that include cross-functional teams, continuous monitoring, and the use of interpretability tools like SHAP and LIME. Regulatory perspectives are also emphasized, specifically detailing FDA guidance on lifecycle oversight and the necessity of transparency in marketing submissions. Furthermore, research indicates a significant awareness-action gap, where theoretical knowledge of fairness often fails to translate into routine clinical practice. Together, these sources advocate for a holistic approach that integrates technical mitigation strategies with institutional accountability to build trust in medical AI.