Fuyi Xie¹ and Carlos Gonzalez², ¹University of California Irvine, USA, ²California State Polytechnic University, USA

Access to high-quality medical data is critical for research but is often hindered by privacy concerns and logistical challenges. GenDataset addresses this problem by developing a generative AI tool that produces synthetic medical data while preserving privacy and statistical integrity [1]. The tool integrates Kaggle for dataset retrieval, Gretel for synthetic data generation, and Firebase for secure storage, all wrapped in a user-friendly web interface. Key challenges included ensuring data utility, scalability, and ease of use, which were addressed through advanced machine learning models, API integrations, and modular design. Experiments demonstrated the tool's ability to generate realistic datasets tailored to user specifications, such as demographics and region. GenDataset improves existing methods by balancing privacy, utility, and accessibility, making it a valuable solution for researchers. Its ability to streamline data collection and ensure compliance with privacy regulations positions it as a transformative tool for advancing medical research and data-driven healthcare innovations [2].

AI, Firebase, Medical, Machine Learning