Cited by 161Artificial Intelligence in Healthcare and EducationPrivacy-Preserving Technologies in DataMachine Learning in Healthcare

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Generative adversarial networks and synthetic patient data: current challenges and future perspectives

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Arora, Anmol, Arora, Ananya·2022·Future Healthcare Journal

DOI 10.7861/fhj.2022-0013PMC PMC9345230

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Abstract

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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Authors: Arora, A., & Arora, A.
Affiliations: University of Cambridge (GB)
ORCID: Arora, Anmol
Arora, Ananya
Venue: Future Healthcare Journal
Publisher: Elsevier BV
Published: 2022-07
Volume: 9
Issue: 2
Pages: 190-193
ISSN: 2514-6645 (ISSN-L)
DOI: 10.7861/fhj.2022-0013
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: other-oa
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 648 d)
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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Generative adversarial networks and synthetic patient data: current challenges and future perspectives

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