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.

深度生成模型扩大了创建逼真合成健康数据集的潜力，但是仍然存在许多问题和挑战，包括如何评估合成数据集与原始真实数据集的相似性和预测效果，以及分享时的隐私风险。本文概述了合成健康数据领域的现状，包括生成和评估方法与工具，实际应用示例，监管和伦理环境，数据访问和治理选项，以及未来发展机会。