ArticleScientific reports2024
Synthetic data for privacy-preserving clinical risk prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Safeguarding biomedical AI: a critical scoping review of privacy-enhancing technologies, hybrid approaches, and deployment models.Frontiers in digital health · 2026Pooled it
- Evaluation of the Applicability of Synthetic Data in the Development of Colorectal Cancer Survival Prediction Models: External Validation of Advanced Machine Learning Models Based on National Cancer Data Center Data.Journal of medical Internet research · 2026Article
- Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research.Nature communications · 2026Article
- Beyond classical models: LLM-driven survival analysis for breast cancer prognosis using European cancer registry data.BMC medical informatics and decision making · 2026Article
- SynthCraft: An AI partner for synthetic data generation to support data access and augmentation in healthcare.PLOS digital health · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- AI-driven synthetic data generation for accelerating hepatology research: A study of the United Network for Organ Sharing (UNOS) database.Hepatology (Baltimore, Md.) · 2026Article
- Assessing the Evolution and Influence of Medical Open Databases on Biomedical Research and Health Care Innovation: A 25-Year Perspective With a Focus on Privacy and Privacy-Enhancing Technologies.Journal of medical Internet research · 2026Article
- Prospective multi-centre evaluation of guideline-based artificial intelligence to streamline multidisciplinary tumour board.Frontiers in oncology · 2026Article
- AI and innovation in clinical trials.NPJ digital medicine · 2025Article
- Generating Synthetic Multi-national Longitudinal Cohorts for Clinically Grounded HIV Research.medRxiv : the preprint server for health sciences · 2025Article
- Fidelity-agnostic synthetic data generation improves utility while retaining privacy.Patterns (New York, N.Y.) · 2025Article
- Explanation and elaboration of MedinAI: guidelines for reporting artificial intelligence studies in medicines, pharmacotherapy, and pharmaceutical services.International journal of clinical pharmacy · 2025Article
- Synthetic healthcare data utility with biometric pattern recognition using adversarial networks.Scientific reports · 2025Article
- Practical implementation of generative artificial intelligence systems in healthcare: A United States perspective.Future healthcare journal · 2024Article
- CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy.IEEE transactions on medical imaging · 2024Article
- Assessment of differentially private synthetic data for utility and fairness in end-to-end machine learning pipelines for tabular data.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Synthetic data promise privacy-preserving data sharing for healthcare research and development. Compared with other privacy-enhancing approaches-such as federated learning-analyses performed on synthetic data can be applied downstream without modification, such that synthetic data can act in place of real data for a wide range of use cases. However, the role that synthetic data might play in all aspects of clinical model development remains unknown. In this work, we used state-of-the-art generators explicitly designed for privacy preservation to create a synthetic version of ever-smokers in the UK Biobank before building prognostic models for lung cancer under several data release assumptions. We demonstrate that synthetic data can be effectively used throughout the medical prognostic modeling pipeline even without eventual access to the real data. Furthermore, we show the implications of different data release approaches on how synthetic biobank data could be deployed within the healthcare system.
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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.