ArticleFrontiers in digital health2025
Synthetic data generation: a privacy-preserving approach to accelerate rare disease research.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic data generation methods for longitudinal and time series health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Quality Assessment and Model Training Impact of Synthetic Images in Medical Diagnostic Imaging: Systematic Review.JMIR AI · 2026Review
- AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.Briefings in bioinformatics · 2026Review
- Synthetic Medical Data vs. The Four Principles: Ethical Trade-Offs and Zombie Data.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Ethical considerations in generative artificial intelligence for synthetic data generation in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome data.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Article
- Exploring Students' Perceptions and Usage of Artificial Intelligence in Supporting Mental Health: A Preliminary Study in Higher Education in Qatar.Healthcare (Basel, Switzerland) · 2026Article
- Data-driven medical devices and the EU MDR: mapping gaps in standards for regulatory compliance.npj health systems · 2026Review
- SHARE: towards usable, trustworthy and interoperable synthetic health data for rare diseases.BMJ health & care informatics · 2026Article
- Machine learning framework for predicting athletic injuries and optimising performance.BMC sports science, medicine & rehabilitation · 2026Article
- AI and innovation in clinical trials.NPJ digital medicine · 2025Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Future Designs of Clinical Trials in Nephrology: Integrating Methodological Innovation and Computational Power.Sensors (Basel, Switzerland) · 2025Review
- On the use of synthetic data for healthcare AI in Africa: Technical performance, governance challenges, and policy readiness.Digital healthReview
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rare disease research faces significant challenges due to limited patient data, strict privacy regulations, and the need for diverse datasets to develop accurate AI-driven diagnostics and treatments. Synthetic data-artificially generated datasets that mimic patient data while preserving privacy-offer a promising solution to these issues. This article explores how synthetic data can bridge data gaps, enabling the training of AI models, simulating clinical trials, and facilitating cross-border collaborations in rare disease research. We examine case studies where synthetic data successfully replicated patient characteristics, and supported predictive modelling and ensured compliance with regulations like GDPR and HIPAA. While acknowledging current limitations, we discuss synthetic data's potential to revolutionise rare disease research by enhancing data availability and privacy file enabling more efficient and effective research efforts in diagnosing, treating, and managing rare diseases globally.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.