The incorporation of Artificial Intelligence (AI) into medical imaging has transformed diagnostic processes, delivering unprecedented speed, accuracy, and precision. This technological leap enables not only early and accurate disease detection but also personalized treatment strategies and optimized resource utilization. Despite its potential, the integration of AI in healthcare presents a complex array of ethical and regulatory challenges. These include safeguarding patient data privacy, addressing biases in AI algorithms, ensuring transparency in decision-making processes, and defining accountability frameworks. This chapter delves into these multifaceted challenges, examining the delicate balance between maximizing the utility of medical imaging data and upholding strict privacy standards. It also explores how biases in training datasets can perpetuate healthcare disparities and compromise outcomes. Furthermore, the chapter highlights the limitations of current regulatory policies, emphasizing the urgent need for globally harmonized frameworks that ensure the safety, efficacy, and interpretability of AI systems. The discussion extends to the critical role of human oversight in AI-assisted diagnostic workflows, underscoring the need for clinicians to remain central in decision-making processes. Real-world case studies and actionable recommendations are presented to guide the development of inclusive, transparent, and accountable AI solutions. Ultimately, the chapter aims to provide a comprehensive roadmap for addressing these ethical and regulatory dimensions, ensuring that AI’s advancements align with healthcare’s foundational principles of equity, safety, and patient-centered care.