# A primer on synthetic health data

arXiv: https://arxiv.org/abs/2401.17653 · HTML: https://mlsanity.com/paper/2401.17653/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 57 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 10 of 10)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Synthetic Data -- Anonymisation Groundhog Day](https://mlsanity.com/paper/2011.07018/) (2020)
- [How Well Generative Adversarial Networks Learn Distributions](https://mlsanity.com/paper/1811.03179/) (2018)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data](https://mlsanity.com/paper/2304.03722/) (2023)
- [On Utility and Privacy in Synthetic Genomic Data](https://mlsanity.com/paper/2102.03314/) (2021)
- [Information estimation using nonparametric copulas](https://mlsanity.com/paper/1807.08018/) (2018)
- [Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?](https://mlsanity.com/paper/2005.13510/) (2020)
- [Bayesian Estimation of Attribute and Identification Disclosure Risks in Synthetic Data](https://mlsanity.com/paper/1804.02784/) (2018)

## Abstract (arXiv)

Recent advances in deep generative models have greatly expanded the potential to create realistic synthetic health datasets. These synthetic datasets aim to preserve the characteristics, patterns, and overall scientific conclusions derived from sensitive health datasets without disclosing patient identity or sensitive information. Thus, synthetic data can facilitate safe data sharing that supports a range of initiatives including the development of new predictive models, advanced health IT platforms, and general project ideation and hypothesis development. However, many questions and challenges remain, including how to consistently evaluate a synthetic dataset's similarity and predictive utility in comparison to the original real dataset and risk to privacy when shared. Additional regulatory and governance issues have not been widely addressed. In this primer, we map the state of synthetic health data, including generation and evaluation methods and tools, existing examples of deployment, the regulatory and ethical landscape, access and governance options, and opportunities for further development.