Generative AI for Synthetic Medical Imaging to Address Data Scarcity

Medical imaging datasets are fundamental for developing reliable artificial intelligence (AI) models in healthcare. However, patient privacy laws, limited sample sizes, and disease rarity often lead to data scarcity, hindering the performance of deep learning algorithms. This study explores the use of Generative AI, particularly Generative Adversarial Networks (GANs) and Diffusion Models, to produce high-fidelity synthetic medical images that augment real-world datasets while preserving patient confidentiality. Through a systematic evaluation on MRI and CT datasets, the paper demonstrates that synthetic data improves diagnostic model accuracy by up to 18% when compared to models trained on limited real data alone. The study concludes that generative AI offers a transformative approach to mitigate data scarcity in medical imaging and accelerate clinical AI deployment under ethical and privacy-conscious frameworks.