Harnessing the power of synthetic data in healthcare: innovation, application, and privacy
Open accessAbstract
Data-driven decision-making in modern healthcare underpins innovation and predictive analytics in public health and clinical research. Synthetic data has shown promise in finance and economics to improve risk assessment, portfolio optimization, and algorithmic trading. However, higher stakes, potential liabilities, and healthcare practitioner distrust make clinical use of synthetic data difficult. This paper explores the potential benefits and limitations of synthetic data in the healthcare analytics context. We begin with real-world healthcare applications of synthetic data that informs government policy, enhance data privacy, and augment datasets for predictive analytics. We then preview future applications of synthetic data in the emergent field of digital twin technology. We explore the issues of data quality and data bias in synthetic data, which can limit applicability across different applications in the clinical context, and privacy concerns stemming from data misuse and risk of re-identification. Finally, we evaluate the role of regulatory agencies in promoting transparency and accountability and propose strategies for risk mitigation such as Differential Privacy (DP) and a dataset chain of custody to maintain data integrity, traceability, and accountability. Synthetic data can improve healthcare, but measures to protect patient well-being and maintain ethical standards are key to promote responsible use.
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Because synthetic data are similar to but not the same as real data, they can be used to eliminate or mask personal identifiers and protect the confidentiality of human participants.
“because synthetic data are similar to but not the same as real data, they can be used to eliminate or mask personal identifiers and protect the confidentiality of human participants [31, 81, 200].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [81]
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- Authors
- Giuffrè, M., & Shung, D. L.
- Affiliations
- University of Trieste (IT)
- Yale University (US)
- Corresponding author
- Giuffrè, Mauro
- Venue
- npj Digital Medicine
- Published
- 2023-10-09
- Volume
- 6
- Issue
- 1
- Pages
- 186-186
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC10562365
- OpenAlex
- W4387457231
- Cited by
- 392
- References
- 62
- Citations by year
- 2026: 135 · 2025: 163 · 2024: 88 · 2023: 6
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ArTS Archivio della ricerca di Trieste (University of Trieste https://www.units.it/) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (UL1TR001863)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- synthetic data, medical data analysis, data privacy, re-identification, differential privacy, digital twin, data bias, data quality, regulatory oversight, transparency and accountability, predictive analytics, patient data protection
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