Synthetic Data Quality Score Calculator

Evaluate synthetic data quality using statistical fidelity, ML utility, and privacy metrics. Determine whether synthetic data is a suitable replacement for real data

Each component has a specific meaning:

• Statistical fidelity — The statistical fidelity recorded for the patient or scenario being assessed.
• ML utility — The ml utility recorded for the patient or scenario being assessed.
• Privacy metrics — The privacy metrics recorded for the patient or scenario being assessed.

Note: Interpret the synthetic data quality result against the clinical thresholds and context described above.

Enter the statistical fidelity, ML utility, privacy metrics for the patient or scenario you are assessing. Evaluate synthetic data quality using statistical fidelity, ML utility, and privacy metrics. Determine whether synthetic data is a suitable replacement for real data. Use the synthetic data quality result to inform your clinical assessment.