How to Evaluate Synthetic Medical Imaging Data? Fidelity, Privacy & Utility

Synthetic medical images have rapidly emerged as a solution to address data scarcity, privacy concerns, and annotation costs in healthcare AI. But generating synthetic data is only half the story—proper evaluation is critical. At Sinkove, we've identified three key criteria for effectively assessing synthetic medical images: realism (fidelity), authenticity (privacy), and utility.

Sinkove recently collaborated on the CheXGenBench project, an extensive benchmarking study led by Raman Dutt, our collaborator from the University of Edinburgh. CheXGenBench provides crucial frameworks for evaluating synthetic medical imaging data. Until now, the community has lacked a common yard-stick: papers reported different metrics, used outdated back-bones, or ignored privacy altogether. CheXGenBench fixes that. The benchmark compares 11 modern text-to-image models, releases a 75 k-image dataset (SynthCheX-75K) on Huggingface and—more importantly—lays down a transparent evaluation recipe with a common set of metrics built on our three pillars: fidelity (realism), privacy (authenticity) and utility. The complete codebase is available on GitHub.

Realism (Fidelity – Visual Realism)

Realism ensures that synthetic images visually and structurally resemble genuine medical data. Highly realistic images help AI models generalize better to real-world conditions. Metrics like the Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) are essential here. However, standard metrics can mislead; always prefer radiology-specific embeddings, as general image metrics may miss nuances critical in medical imaging. Additionally, metrics that check the diversity and coverage of pathologies are crucial—high realism isn't beneficial if only a limited set of scenarios are represented.

Authenticity (Privacy – Patient Safety)

Privacy evaluation verifies that synthetic images don't inadvertently expose sensitive patient information. Despite being synthetic, data can unintentionally replicate specific patient features, creating privacy risks. Metrics such as deep re-identification (re-ID) scores and latent-space distances help quantify how much synthetic images deviate from the original patient data. Prioritizing privacy isn't just ethical—it's necessary for compliance with healthcare regulations.

Utility (Task‑Specific Performance)

Utility measures whether synthetic images serve their intended clinical or research purposes effectively. This evaluation is highly context-dependent. For example:

• Classification tasks: assess with accuracy or AUROC.
• Segmentation tasks: dice coefficient or intersection-over-union (IoU).
• Diagnostic support: metrics like sensitivity, specificity, or clinical outcomes.

Utility cannot be generalized across tasks; it requires precise definitions aligned with clinical or operational goals. Always benchmark synthetic datasets against real data baselines to validate effectiveness.

Why This Matters to Sinkove

At Sinkove, our mission revolves around generating robust synthetic datasets that empower medical AI systems. Evaluating synthetic medical imaging data using the realism–privacy–utility framework isn't just best practice—it's essential. Leveraging this rigorous evaluation ensures our synthetic data reliably enhances clinical outcomes and maintains regulatory compliance.

By thoughtfully evaluating synthetic images with these three criteria, we can confidently unlock the full potential of AI in healthcare—delivering innovations that genuinely matter.