ENHANCING HEALTHCARE AI MODELS WITH SYNTHETIC DATA: SOLUTIONS FOR LIMITED DATA IN DISEASE PREDICTION AND TREATMENT

Abstract

This article explores the transformative potential of synthetic data in addressing the challenges of limited data availability in healthcare AI development. It examines various techniques for generating synthetic data, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and the Synthetic Minority Over-sampling Technique (SMOTE), and their applications in enhancing disease prediction and treatment optimization models. Through case studies, the article demonstrates how synthetic data can improve rare disease diagnosis, optimize clinical trial design, and enhance predictive models for chronic diseases. The discussion encompasses the strengths of synthetic data in healthcare AI, such as addressing data scarcity and privacy concerns, as well as its limitations, including potential biases and validation challenges. The article concludes by outlining future directions for synthetic data in healthcare, emphasizing its role in advancing personalized medicine and fostering more inclusive and collaborative research environments.