Generative Artificial Intelligence (GenAI) is rapidly transforming the healthcare landscape by enabling novel solutions in areas such as medical imaging, drug discovery, and synthetic data generation. This systematic review analyzes 87 original research articles to provide a comprehensive overview of how GenAI models including Generative Adversarial Networks (GANs), Diffusion Models, Large Language Models (LLMs), and Variational Autoencoders (VAEs)—are applied across various healthcare domains. We investigate key aspects such as the most frequently utilized generative models, their primary applications, the datasets that support their development, and the evaluation metrics used to measure their performance. Our analysis reveals that GANs, Diffusion Models, LLMs, and VAEs are the dominant GenAI architectures employed in current healthcare research. Each included study is summarized to highlight its core contributions, offering valuable insights into the practical use of GenAI in clinical and biomedical settings. By focusing exclusively on peer-reviewed original research, this review ensures the rigor and relevance of its findings. The study serves as a foundational resource for researchers and practitioners, outlining the current landscape and identifying promising directions for future investigation in GenAI-powered healthcare innovation.

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located.