Abstract

The growing adoption of artificial intelligence (AI) in healthcare presents both transformative opportunities and unprecedented governance challenges. From diagnostic imaging to predictive analytics, AI-driven tools now influence clinical decisions, patient outcomes, and institutional efficiency. However, these innovations also introduce regulatory, ethical, and technical complexities surrounding privacy, security, explainability, and accountability. This paper explores a holistic framework for secure AI governance in healthcare, emphasizing compliance with global regulations such as the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), and the U.S. Food and Drug Administration (FDA) AI/ML guidelines. The proposed framework integrates data governance, model lifecycle management, and auditability mechanisms to ensure end-to-end trust and transparency. Through a review of existing literature and emerging practices, this study identifies key components of effective AI governance, including data lineage tracking, bias mitigation, automated compliance monitoring, and federated learning for privacy preservation. The paper also presents architectural recommendations for implementing governance controls across the machine learning (ML) lifecycle—from data ingestion to model deployment—while balancing innovation with regulatory adherence. The findings underscore the need for an adaptive, risk-based governance model to support responsible AI adoption in regulated clinical environments.