ReviewNPJ digital medicine2023
Harnessing the power of synthetic data in healthcare: innovation, application, and privacy.
Review in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 158 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
158 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Pedagogical Efficacy of LLM-Generated Synthetic Data Versus Real-World Clinical Records: A Randomized Controlled Non-Inferiority Trial.Perspectives on medical education · 2026Trial
- Large language model-based triage to identify antiretroviral therapy adherence barriers and risk levels in patient messages.JAMIA open · 2026Article
- Synthetic-to-Clinical Ensemble Learning for Volumetric Breast Tumor Segmentation in Digital Breast Tomosynthesis Under Limited Annotated Data.Diagnostics (Basel, Switzerland) · 2026Article
- Digital bioethics: exploring an emerging field.Medicine, health care, and philosophy · 2026Article
- Validating methods for inferring co-occurring diseases: a flexible framework for simulating synthetic data.BMC medical research methodology · 2026Article
- Large reasoning models as thinking machines for medicine.Nature biomedical engineering · 2026Review
- PRISM-G: an interpretable privacy scoring framework for assessing risk in synthetic human genome data.Bioinformatics (Oxford, England) · 2026Article
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Ethical and Legal Concerns of Deepfake Technology in Biomedical Imaging: A Comprehensive Survey.Annals of biomedical engineering · 2026Review
- Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.JMIR cardio · 2026Review
- A Large Language Model-Driven System for Advance Care Planning Training Among Health Care Providers in the Chinese Context: Development and Technical Evaluation.Journal of medical Internet research · 2026Article
- 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
- From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Evaluating the quality of tabular synthetic data in health care.PLOS digital health · 2026Article
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Navigating AI and machine learning in cancer research: an end-to-end translational framework.Journal of translational medicine · 2026Review
- Generating synthetic multi-national longitudinal cohorts for clinically grounded HIV research.Nature communications · 2026Article
- Generating Brain MRI with StyleGAN2-ADA: The Effect of the Training Set Size on the Quality of Synthetic Images.Journal of imaging informatics in medicine · 2026Article
98 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
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.