Synthetic Data Generation: Unlocking New Horizons for Medical Imaging AI

Many clinical specialties outside of traditional radiology, such as ophthalmology, have become major producers of imaging data, yet their imaging environments often evolve into fragmented ecosystems with multiple vendors, proprietary formats, and disconnected storage. These conditions can create workflow friction, delays that disrupt provider–patient interactions, and data degradation that limits longitudinal comparison and future AI readiness.

In this session, attendees will learn from a real-world case study demonstrating how a large academic ophthalmology department confronted and resolved these challenges. Presenters will guide participants through four key learning components that illustrate both the problem and the transformation strategy:

• The Current State: A Case Study in Workflow Friction – Understanding daily operational challenges, including slow image transfer times, inconsistent data entry, and workarounds caused by lack of standardization.
• The Ideal State: Evidence-Based Best Practices – Exploring foundational enterprise imaging principles such as multi-disciplinary governance, DICOM and DMWL-based data standardization, and consolidating clinical imaging solutions onto an enterprise platform.
• The Journey: A Playbook of Practical Steps – Mapping best practices to actionable steps, including building baseline assessments, improving data integrity, collaborating with the EMR team to standardize order names, and maturing workflows to align with long-term strategic goals.  

Demonstrating how organizations can move from departmental fragmentation to enterprise cohesion through phased governance, source-level data standardization, workflow integration, and technology consolidation.

By the end of the session, attendees will understand how systematic, evidence-based improvements can transform siloed departmental imaging into a cohesive enterprise service that supports current clinical needs and future AI and research innovation.

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.