Enhancing Predictive Performance under Data Scarcity: The Role of Generative AI-Based Synthetic Data

Abstract

In an era where predictive analytics drives decision-making in healthcare and industrial operations, data scarcity emerges as a formidable barrier, limiting the development of reliable models for forecasting disease progression or machinery failures. Generative AI, through techniques like Generative Adversarial Networks, Variational Autoencoders, Diffusion Models, and Transformers, offers a sophisticated means to create synthetic data that mirrors real-world patterns, thereby augmenting limited datasets without compromising privacy or ethical standards. This comprehensive literature review, grounded in high-impact peer-reviewed journals, explores these models’ applications in addressing data limitations, emphasizing qualitative enhancements in model robustness, generalization, and interpretability. Tailored for an MBA audience, it highlights business implications such as cost efficiencies in data acquisition, innovations in personalized healthcare services, and sustainable manufacturing practices. Challenges including bias propagation, privacy risks, and model instability are discussed broadly, alongside future directions toward hybrid, privacy-preserving frameworks that foster ethical AI deployment in competitive markets.