Ensuring safe adoption of AI tools in healthcare hinges on access to
sufficient data for training, testing and validation. In response to privacy
concerns and regulatory requirements, using synthetic data has been suggested.
Synthetic data is created by training a generator on real data to produce a
dataset with similar statistical properties. Competing metrics with differing
taxonomies for quality evaluation have been suggested, resulting in a complex
landscape. Optimising quality entails balancing considerations that make the
data fit for use, yet relevant dimensions are left out of existing frameworks.
We performed a comprehensive literature review on the use of quality evaluation
metrics on SD within the scope of tabular healthcare data and SD made using
deep generative methods. Based on this and the collective team experiences, we
developed a conceptual framework for quality assurance. The applicability was
benchmarked against a practical case from the Dutch National Cancer Registry.
We present a conceptual framework for quality assurance of SD for AI
applications in healthcare that aligns diverging taxonomies, expands on common
quality dimensions to include the dimensions of Fairness and Carbon footprint,
and proposes stages necessary to support real-life applications. Building trust
in synthetic data by increasing transparency and reducing the safety risk will
accelerate the development and uptake of trustworthy AI tools for the benefit
of patients. Despite the growing emphasis on algorithmic fairness and carbon
footprint, these metrics were scarce in the literature review. The overwhelming
focus was on statistical similarity using distance metrics while sequential
logic detection was scarce. A consensus-backed framework that includes all
relevant quality dimensions can provide assurance for safe and responsible
real-life applications of SD.

通过综合文献回顾和团队经验，我们提出了一个概念性框架，以保证合成数据在医疗人工智能应用中的质量，并扩展了普遍质量维度，包括公平性和碳足迹，并提出了支持实际应用所必需的阶段，通过增加透明度和减少安全风险来增强对合成数据的信任，以加速可信任的医疗人工智能工具的发展和推广，促进患者受益。