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Generative adversarial networks and synthetic patient data: current challenges and future perspectives
Open accessAbstract
Artificial intelligence (AI) has been heralded as one of the key technological innovations of the 21st century. Within healthcare, much attention has been placed upon the ability of deductive AI systems to analyse large datasets to find patterns that would be unfeasible to program. Generative AI, including generative adversarial networks, are a newer type of machine learning that functions to create fake data after learning the properties of real data. Artificially generated patient data has the potential to revolutionise clinical research and protect patient privacy. Using novel techniques, it is increasingly possible to fully anonymise datasets to the point where no datapoint is traceable to any real individual. This can be used to expand and balance datasets as well as to replace the use of real patient data in certain contexts. This paper focuses upon three key uses of synthetic data: clinical research, data privacy and medical education. We also highlight ethical and practical concerns that require consideration.
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“Generative AI aims to address various medical conditions, from chronic diseases like diabetes to acute conditions like stroke [6, 38, 60].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [6]
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“Generative AI models, such as GANs, can be used for tasks like synthesising medical images or generating patient data [6, 37).”
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“10 Similarly, GANs may also be used in the construction of synthetic digital twins, whereby an artificial construction of a system is created that preserves all the patterns of the true system.”
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Metadata
- Authors
- Arora, A., & Arora, A.
- Affiliations
- University of Cambridge (GB)
- Venue
- Future Healthcare Journal
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 9
- Issue
- 2
- Pages
- 190-193
- ISSN
- 2514-6645 (ISSN-L)
- PMC
- PMC9345230
- OpenAlex
- W4288455572
- Semantic Scholar
- 5c56849181d2871dedb95f3bda443dc11d29a399
- Cited by
- 161
- References
- 18
- Citations by year
- 2026: 20 · 2025: 58 · 2024: 51 · 2023: 31 · 2022: 1
- FWCI
- 10.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Healthcare Journal (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- generative adversarial networks, synthetic patient data, synthetic data generation, clinical research, data privacy, data anonymization, data balancing, medical education, ethical considerations, artificial intelligence in healthcare
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