The integration of generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), into medical imaging has revolutionized diagnostic processes. However, the opacity of these models poses significant challenges for clinical adoption. This paper delves into the imperative of interpretability in generative AI, exploring techniques like saliency mapping, attention mechanisms, and feature attribution to elucidate model decisions. Through real-world case studies, we examine the application of these methods in enhancing diagnostic precision and fostering clinician trust. We also discuss the ethical and regulatory considerations essential for the responsible deployment of interpretable AI in healthcare. Our findings underscore the necessity of transparent AI systems to bridge the gap between advanced computational models and clinical practice. The integration of interpretability in generative AI not only improves model transparency but also facilitates the adoption of AI technologies in healthcare settings. By addressing concerns related to accountability and trust, interpretable AI can pave the way for widespread acceptance and utilization in medical decision-making processes. Furthermore, regulatory bodies must establish guidelines for the ethical use of interpretable AI to ensure patient privacy and data security are protected. It is crucial for healthcare providers to prioritize the development and implementation of interpretable AI solutions that align with these regulatory standards to maximize the benefits of AI technology in improving patient outcomes.