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Advancements in generative artificial intelligence (AI) are setting the stage for transformative changes in medical imaging, particularly through the development of the Virtual Scanner. This innovative approach leverages resilient generative AI to synthesize radiological images, addressing critical challenges in the field such as data scarcity, patient exposure to radiation, and the limitations of current imaging technologies. By harnessing the power of Generative Adversarial Networks (GANs) and focusing on the resilience of these algorithms, the Virtual Scanner aims to enhance diagnostic accuracy, improve patient care, and fill gaps in multimodal datasets. Our research explores both unimodal and multimodal techniques, including GAN ensembles, latent augmentation, and advanced texture synthesis, to create robust and adaptable generative models. Through extensive experimentation and analysis, we demonstrate the potential of the Virtual Scanner to revolutionize medical diagnostics by providing a safer, more efficient, and comprehensive imaging solution. The implications of this work extend beyond immediate medical applications, offering insights into the development of AI technologies capable of navigating the complexities of real-world data.