Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare

Synthetic data quality requires a comprehensive framework beyond statistical similarity: The paper identifies that existing evaluation metrics are fragmented with "competing metrics with differing taxonomies," making it difficult to assess whether synthetic healthcare data is truly safe to use. A unified framework is needed that goes beyond just checking if the synthetic data has similar statistical properties to the real data.

Fairness and environmental impact must be included in quality assessments: The framework expands evaluation criteria to include dimensions often overlooked in existing approaches—specifically Fairness (ensuring the synthetic data doesn't perpetuate or amplify biases) and Carbon footprint (the computational cost of generating synthetic data). This reflects a more holistic view of responsible AI development.

Multiple stages of quality assurance are necessary for real-world healthcare applications: Rather than a single evaluation step, the paper proposes staged quality assurance processes to support practical deployment. This staged approach helps ensure synthetic data is genuinely "fit for use" in clinical settings, not just theoretically sound.

Building transparency and trust is essential for adoption: The authors emphasize that "building trust in synthetic data by increasing transparency and reducing the safety risk will accelerate the development and uptake of trustworthy AI tools." This suggests that responsible communication about synthetic data limitations—not just its benefits—is critical for healthcare stakeholders.

The framework was validated against real healthcare data: The applicability of the proposed framework was tested using data from the Dutch National Cancer Registry, demonstrating that these quality considerations are practical and relevant to actual healthcare AI applications.