Generative AI for Synthetic Medical Imaging Data Augmentation

Pralohith Reddy Chinthalapelly (), Srinivas Bangalore Sujayendra Rao () and Vijaya Bhaskara Rao Kotapati ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 2, issue 1, 344-367

Abstract: Medical imaging plays a crucial role in modern diagnostics, yet the development of robust machine learning models is often constrained by limited annotated datasets. Generative Artificial Intelligence (AI), particularly generative adversarial networks (GANs) and diffusion models, has emerged as a powerful tool for synthesizing realistic medical images that can address data scarcity. This study explores the role of generative AI in medical imaging data augmentation, highlighting its ability to generate diverse, high-quality, and clinically relevant synthetic datasets. We discuss key methodologies, applications across modalities such as MRI, CT, and X-ray, and evaluate the potential benefits in improving diagnostic accuracy, reducing model bias, and accelerating clinical AI research. Furthermore, challenges related to validation, interpretability, ethical concerns, and regulatory acceptance are examined. By synthesizing current advancements, this research underscores how generative AI-driven augmentation can significantly enhance medical imaging pipelines, paving the way for more generalizable and equitable AI models in healthcare.

Keywords: Generative AI; Synthetic Data; Medical Imaging; Data Augmentation; GANs; Diffusion Models (search for similar items in EconPapers)
Date: 2024
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