Applications of Generative Artificial Intelligence in Brain MRI Image Analysis for Brain Disease Diagnosis

Abstract

The brain is vulnerable to diseases, including infections, injuries, and tumors, that can substantially influence daily life and health; therefore, early diagnosis and treatment are necessary. MRI, because of its ability to detect early abnormalities without interference, is crucial for evaluating brain structure and function. Generative artificial intelligence (GAI) can model disease characteristics in brain MRI images, thereby increasing diagnostic accuracy by comparing healthy and diseased brains. This review examines the transformative role of GAI in analyzing brain MRI images for diagnosing brain diseases. The study explores five foundational GAI models—generative adversarial networks, diffusion models, transformers, variational autoencoders, and the autoregressive model—and their applications in MRI imaging. These models enhance data preprocessing, image segmentation, and feature extraction, thereby increasing diagnostic accuracy and supporting early disease detection. This review highlights GAI’s superiority in addressing data scarcity issues, enhancing image quality, and providing comprehensive insights into brain disease pathology; it additionally discusses promising directions for future research.