About the Session
Artificial intelligence in medical imaging continues to advance, but progress is often slowed by limited access to large, diverse, and well-annotated datasets. Privacy concerns, regulatory requirements, and institutional data silos make it difficult to share real-world imaging data at scale. This session addresses that gap by introducing attendees to synthetic imaging data as a practical solution for expanding datasets while protecting patient privacy and supporting responsible AI development.
Participants will learn the core techniques behind synthetic data generation, including GANs and diffusion models, and how these approaches can be applied to augment datasets, reduce class imbalance, and improve model robustness and generalizability. Attendees will explore real-world applications such as modality translation, contrast synthesis, explainable AI, and the use of synthetic cases for education and training. The session also emphasizes how synthetic data can enable privacy-preserving data sharing across institutions while aligning with regulatory frameworks such as HIPAA and GDPR.
Beyond the technical benefits, participants will gain a clear understanding of the risks and limitations associated with synthetic data, including bias amplification, hallucinated features, and reidentification concerns. Engagement will include live polling and interactive discussion, giving attendees the opportunity to reflect on their own use cases, ask practical questions, and leave with a balanced, informed perspective on when and how synthetic data can be responsibly integrated into medical imaging AI pipelines.
Objectives
- Describe the fundamental techniques for generating synthetic medical imaging data, including GANs and diffusion models.
- Explain how synthetic data can improve model performance and enable privacy-preserving data sharing.
- Identify potential risks, including data bias, identity leakage, and regulatory challenges when using synthetic data in AI development.
Presented By
