Applying Generative mock Neuro Forge Networks for Synthetic Data Generation in AI Healthcare Systems

Abstract

The rapid advancement of artificial intelligence (AI) in healthcare has catalyzed the need for large, diverse, and high-quality datasets to train robust machine learning models. However, acquiring real-world medical data presents challenges due to privacy concerns, regulatory restrictions, and data scarcity. Generative mock NeuroForge Networks (GMNFNs) offer a promising solution by enabling synthetic data generation that mimics the complexity and variability of real-world datasets while preserving patient confidentiality. This paper introduces a novel three-step framework for synthetic data generation in AI healthcare systems: (1) HoloScope Sampling—a pre-processing algorithm that ensures input data diversity and represents the full spectrum of real-world scenarios; (2) Generative mock NeuroForge Networks (GMNFNs)—a cutting-edge architecture designed to generate high-fidelity synthetic datasets while addressing privacy and ethical constraints; and (3) Fuzzy press DataTrust Validator (FPDTV)—a post-generation algorithm that quantitatively evaluates the reliability and utility of synthetic datasets using advanced statistical and domain-specific metrics. By integrating these steps, this research demonstrates a pathway to bridge data gaps, enhance model performance, and mitigate biases in healthcare AI systems. Ethical considerations and the integration of these algorithms into existing frameworks are discussed, providing a roadmap for accelerating innovation while adhering to privacy and regulatory standards.