Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications

•The most frequent main healthcare domains are Oncology, Neurology, and Cardiology.•Key motivations include data quality, data scarcity, and AI development.•Future use cases focus on AI development, and enhancing clinical knowledge.•Unstructured data, particularly images, is the most commonly generated data type. The development of Artificial Intelligence in the healthcare sector is generating a great impact. However, one of the primary challenges for the implementation of this technology is the access to high-quality data due to issues in data collection and regulatory constraints, for which synthetic data is an emerging alternative. While previous research has reviewed synthetic data generation techniques, there is limited focus on their applications and the motivations driving their synthesis. A comprehensive review is needed to expand the potential of synthetic data into less explored healthcare areas. This review aims to identify the healthcare domains where synthetic data are currently generated, the motivations behind their creation, their future uses, limitations, and types of data. Following the PRISMA-ScR framework, this review analysed literature from the last 10 years within PubMed, Scopus, and Web of Science. Reviews containing information on synthetic data generation in healthcare were screened and analysed. Key healthcare domains, motivations, future uses, and gaps in the literature were identified through a structured data extraction process. Of the 346 reviews identified, 42 were included for data extraction. Thirteen main domains were identified, with Oncology, Neurology, and Cardiology being the most frequently mentioned. Five primary motivations for synthetic data generation and three major categories of future applications were highlighted. Additionally, unstructured data, particularly images, were found to be the predominant type of synthetic data generated. Synthetic data are currently being generated across diverse healthcare domains, showcasing their adaptability and potential. Despite their early stage, synthetic data technologies hold significant promise for future applications. Expanding their use into new domains and less common data types (e.g., video and text) could further enhance their impact. Future work should focus on developing evaluation benchmarks and standardized generative models tailored to specific healthcare domains.