The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
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).”
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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Cites
Language Models are Few-Shot Learners
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Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
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Addressing bias in big data and AI for health care: A call for open science
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The Clinician and Dataset Shift in Artificial Intelligence
Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I. S., & Saria, S. (2021) · New England Journal of Medicine · doi:10.1056/nejmc2104626 · Cited by 855
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Synthetic data in machine learning for medicine and healthcare
Chen, R. J., Lu, M. Y., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00751-8 · Cited by 728
Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation
Marcel, S., & Millán, J. D. R. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1012 · Cited by 538
AI-generated characters for supporting personalized learning and well-being
Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00417-9 · Cited by 358
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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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Cited by
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A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915
Reddy, S. (2024) · Implementation Science · doi:10.1186/s13012-024-01357-9 · Cited by 554
“Generative AI aims to address various medical conditions, from chronic diseases like diabetes to acute conditions like stroke [6, 38, 60].”
BackgroundMethodZushin, P.-J. H., Mukherjee, S., & Wu, J. C. (2023) · Journal of Clinical Investigation · doi:10.1172/jci175824 · Cited by 347
The value of standards for health datasets in artificial intelligence-based applications
Arora, A., Alderman, J. E., Palmer, J., Ganapathi, S., Laws, E., McCradden, M. D., Oakden‐Rayner, L., Pfohl, S. R., Ghassemi, M., McKay, F., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02608-w · Cited by 341
Self-citationLa nueva realidad de la educación ante los avances de la inteligencia artificial generativa
García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2023) · RIED-Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.27.1.37716 · Cited by 238
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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