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

TL;DR

This paper develops a comprehensive quality assessment framework for synthetic tabular healthcare data, addressing existing gaps by including fairness and carbon footprint metrics to ensure trustworthy AI applications.

Contribution

It introduces a new conceptual framework that aligns diverse quality metrics, expands quality dimensions, and benchmarks it with real healthcare data.

Findings

Framework includes fairness and carbon footprint metrics.

Benchmarking on Dutch National Cancer Registry data.

Focuses on transparency and safety in synthetic data use.

Abstract

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.