Generative artificial intelligence (AI) has emerged as a transformative force in biomedical imaging and smart health informatics, revolutionizing healthcare delivery and patient outcomes. This paper explores the profound impact of generative AI across several key areas, including enhanced diagnostic capabilities achieved through techniques like generative adversarial networks (GANs) and variational autoencoders (VAEs), which significantly improve diagnostic accuracy by synthesizing realistic medical images, denoising noisy data, and reconstructing missing image regions. Personalized healthcare is also made possible by generative AI algorithms, which use thorough analysis of medical imaging data to customized treatment plans to each patient’s needs. This results in more accurate interventions, better patient outcomes, and more efficient use of resources. Additionally, generative AI speeds up biomedical imaging research and development, encouraging innovation and advancing healthcare delivery by producing synthetic data for training and validating new algorithms. To guarantee responsible AI adoption, however, strong ethical frameworks and governance structures are necessary, addressing issues like patient privacy, bias mitigation, and transparency to foster stakeholder confidence. In order to successfully apply generative AI in biomedical imaging, industry, academia, healthcare providers, and regulators must work together to promote information sharing, innovation, and the application of research discoveries in clinical settings. In summary, generative AI has a significant impact on biomedical imaging and smart health informatics, offering the potential to transform healthcare delivery and open the door for future smarter, more effective healthcare systems through enhanced diagnostic accuracy, individualized patient care, accelerated research and development, and ethical governance.