This white paper outlines a comprehensive approach to generating synthetic medical data, emphasizing its value in preserving privacy, enabling realistic simulations, and supporting AI model development. It reviews key methods like statistical modeling, GANs, and simulation models. Applications include clinical trial simulation, machine learning training, public health, and decision support. Challenges include ensuring realism, fairness, and regulatory compliance. Future directions focus on advanced models, dynamic data generation, and interdisciplinary research to unlock the full potential of synthetic data in healthcare.

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