Generative AI in Neuroimaging: Advancing Brain MRI Analysis and Interpretation
TL;DR
Overall, the reviewed literature demonstrates that generative AI has substantial potential to improve brain MRI analysis through realistic data generation, enhanced image quality, and more informative feature representations.
Abstract
Recent advances in generative artificial intelligence (AI) have shown significant promise for brain magnetic resonance imaging (MRI), enabling applications such as image synthesis, modality translation, reconstruction, super-resolution, segmentation, anomaly detection, and disease identification. This PRISMA-ScR-guided scoping review provides a structured synthesis of recent peer-reviewed studies on generative AI for brain MRI analysis published between January 2024 and March 2026. A total of 43 studies meeting predefined inclusion criteria were analyzed. We review major generative architectures, including variational autoencoders (VAEs), generative adversarial networks (GANs), diffusion models, and transformer-based generative models, and summarize their applications across key neuroimaging tasks. We also provide an overview of the publicly available datasets commonly used for model development and evaluation. Beyond reporting performance, this review critically examines the current evidence with respect to reproducibility, external validation, data availability, evaluation validity, data leakage, hallucination and safety risks, and barriers to clinical translation. Although many studies report promising results on retrospective benchmark datasets, external validation, prospective evaluation, reader studies, and clinically oriented assessments remain relatively uncommon. Challenges related to generalization, dataset heterogeneity, computational requirements, privacy, and regulatory considerations continue to limit real-world deployment. Overall, the reviewed literature demonstrates that generative AI has substantial potential to improve brain MRI analysis through realistic data generation, enhanced image quality, and more informative feature representations. However, the current evidence primarily supports technical feasibility and methodological advances rather than established clinical utility. We conclude by identifying key research gaps and future research directions toward more robust, interpretable, reproducible, and clinically translatable generative AI frameworks for brain MRI analysis.