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Ensuring safe adoption of AI tools in healthcare hinges on access tosufficient data for training, testing and validation. In response to privacyconcerns and regulatory requirements, using synthetic data has been suggested.Synthetic data is created by training a generator on real data to produce adataset with similar statistical properties. Competing metrics with differingtaxonomies for quality evaluation have been suggested, resulting in a complexlandscape. Optimising quality entails balancing considerations that make thedata fit for use, yet relevant dimensions are left out of existing frameworks.We performed a comprehensive literature review on the use of quality evaluationmetrics on SD within the scope of tabular healthcare data and SD made usingdeep generative methods. Based on this and the collective team experiences, wedeveloped a conceptual framework for quality assurance. The applicability wasbenchmarked against a practical case from the Dutch National Cancer Registry.We present a conceptual framework for quality assurance of SD for AIapplications in healthcare that aligns diverging taxonomies, expands on commonquality dimensions to include the dimensions of Fairness and Carbon footprint,and proposes stages necessary to support real-life applications. Building trustin synthetic data by increasing transparency and reducing the safety risk willaccelerate the development and uptake of trustworthy AI tools for the benefitof patients. Despite the growing emphasis on algorithmic fairness and carbonfootprint, these metrics were scarce in the literature review. The overwhelmingfocus was on statistical similarity using distance metrics while sequentiallogic detection was scarce. A consensus-backed framework that includes allrelevant quality dimensions can provide assurance for safe and responsiblereal-life applications of SD.

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