As Generative AI (GenAI) technologies such as Generative Adversarial Networks (GANs) and Large Language Models (LLMs) gain prominence, concerns around transparency, accountability, privacy, and bias become increasingly critical. This chapter explores how these AI systems, while offering transformative capabilities, complicate classic ethical principles like autonomy, beneficence, and non-maleficence. The privacy risks linked to large-scale data utilization in GenAI, including de-identification limitations and the potential for re-identification, were explored as well. We highlight the essential role of Explainable AI (XAI) in building trust, ensuring fairness, and supporting ethical decision-making in healthcare. Furthermore, this chapter discusses emerging global regulatory frameworks, such as the FDA’s oversight and GDPR’s data protection regulations, which aim to mitigate risks while promoting innovation. Through critical analysis, we underscore the need for a balanced approach that leverages GenAI’s potential while adhering to stringent ethical and regulatory standards to ensure equitable and safe healthcare applications.