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The project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 101034369. This joint undertaking receives support from the European Union's Horizon 2020 Research and Innovation Programme, the European Federation of Pharmaceutical Industries and Associations (EFPIA) and Link2Trials. This communication reflects the views of the authors and neither the IMI nor the European Union, EFPIA, or Link2Trials are liable for any use that may be made of the information contained herein.

Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Rujas, MiguelCorresponding AuthorMartín Gómez Del Moral Herranz RAuthorFico, GiuseppeAuthorMerino-Barbancho, BeatrizAuthor

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January 19, 2025
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Synthetic data generation in healthcare: A scoping review of reviews on domains, motivations, and future applications

Publicated to:International Journal Of Medical Informatics. 195 105763- - 2025-03-01 195(), DOI: 10.1101/2024.08.09.24311338

Authors: Rujas, Miguel; Fico, Giuseppe; Merino-Barbancho, Beatriz

Affiliations

Univ Politecn Madrid, Life Supporting Technol Res Grp, Avda Complutense 30, Madrid 28040, Spain - Author

Abstract

Background: 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. Objective: 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. Materials and methods: 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. Results: 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. Discussion and conclusion: 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.

Keywords

Adversarial networkArtificial intelligenceArtificial-intelligenceBenchmarkingCardiologyData collectionData extractionFuture applicationsGenerative modelHealth careHealth care costHealthcarHealthcareHealthcare domainsHealthcare sectorsHigh quality dataHumanIndustry, innovation and infrastructureMotivationPreferred reporting items for systematic reviews and meta-analysesReviewScoping reviewSynthetic dataSynthetic data generationSynthetic data generationsUmbrella reviewVideorecordingWeb of science

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal International Journal Of Medical Informatics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 16/44, thus managing to position itself as a Q1 (Primer Cuartil), in the category Medical Informatics.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-05:

  • WoS: 1

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-05:

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 9 - Industry, innovation and infrastructure, with a probability of 42% according to the mBERT algorithm developed by Aurora University.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (RUJAS ATAHONERO, MIGUEL) and Last Author (MERINO BARBANCHO, BEATRIZ).

the author responsible for correspondence tasks has been RUJAS ATAHONERO, MIGUEL.