ReviewiScience2022
Synthetic data as an enabler for machine learning applications in medicine.
Review in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of data sources for artificial intelligence applications in pediatric brain tumors in Europe: implications for bias and generalizability.Frontiers in oncology · 2023Pooled it
- Synthetic Content Validation of Pediatric Trust Instruments Using Persona-Driven Large Language Models: Development and Evaluation Study.JMIR formative research · 2026Article
- Refined Exclusion in Medical AI: Data Justice and Patient Safety Governance.Journal of medical Internet research · 2026Article
- Review
- Primary Graft Dysfunction After Lung Transplantation: A Temporal Classification and Machine Learning Clustering.Transplantation direct · 2026Article
- Synthetic cervical spine radiographs: expert validation and transfer learning for low-data settings.BMC medical informatics and decision making · 2026Article
- AI-driven synthetic data generation for accelerating hepatology research: A study of the United Network for Organ Sharing (UNOS) database.Hepatology (Baltimore, Md.) · 2026Article
- Rising Role of Artificial Intelligence in Clinical Pharmacometrics and Model-Informed Precision Dosing in Pediatrics.The journal of pediatric pharmacology and therapeutics : JPPT : the official journal of PPAG · 2026Article
- Machine Learning Modeling for Predicting Infliximab Pharmacokinetics in Pediatric and Young Adult Patients With Crohn Disease: Leveraging Ensemble Modeling With Synthetic and Real-World Data.Therapeutic drug monitoring · 2026Article
- SHARE: towards usable, trustworthy and interoperable synthetic health data for rare diseases.BMJ health & care informatics · 2026Article
- Virtual agents as a scalable tool for diverse, robust gesture recognition.Behavior research methods · 2026Article
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- Synthetic data within a common data model for artificial intelligence applications in maternal health: experience report in the Colombian contextBiomedica : revista del Instituto Nacional de Salud · 2025Article
- In silico generation of synthetic cancer genomes using generative AI.Cell genomics · 2025Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer.JCO clinical cancer informatics · 2025Article
- FISM: harnessing deep learning and reinforcement learning for precision detection of microaneurysms and retinal exudates for early diabetic retinopathy diagnosis.BioData mining · 2025Article
- Article
- Digital twins, synthetic patient data, and in-silico trials: can they empower paediatric clinical trials?The Lancet. Digital health · 2025Review
- Validity of tremor analysis using smartphone compatible computer vision frameworks.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic data generation is the process of using machine learning methods to train a model that captures the patterns in a real dataset. Then new or synthetic data can be generated from that trained model. The synthetic data does not have a one-to-one mapping to the original data or to real patients, and therefore has the potential of privacy preserving properties. There is a growing interest in the application of synthetic data across health and life sciences, but to fully realize the benefits, further education, research, and policy innovation is required. This article summarizes the opportunities and challenges of SDG for health data, and provides directions for how this technology can be leveraged to accelerate data access for secondary purposes.
Indexed as
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.