ArticleJCO clinical cancer informatics2023
Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology.
Article in JCO clinical cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- 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
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Next-generation synthetic trials in hematology with generative artificial intelligence.Leukemia · 2026Review
- Leveraging Synthetic Clinical Data for Validation and Operational Readiness in Clinical Trials.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Clinical Nutrition: Current Uses, Challenges, and Opportunities.Nutrients · 2026Review
- Review
- Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment.Scientific data · 2026Article
- Evaluating the quality of tabular synthetic data in health care.PLOS digital health · 2026Article
- Molecular-Based Ecosystem to Improve Personalized Medicine in Chronic Myelomonocytic Leukemia.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 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
- Predicting postoperative delirium after lung cancer resection: the utility of synthetic data and LIME algorithm for model interpretation.Scientific reports · 2026Article
- Machine learning models in predictive factors for megaloblastic character of macrocytic anemia.Leukemia research reports · 2026Article
- Transparency of medical artificial intelligence systems.Nature reviews bioengineering · 2026Article
- Zero-Knowledge Process Verification: A Comprehensive Framework for a Distributed Healthcare System.Blockchain in healthcare today · 2026Article
- GAN-enhanced digital twins for biomarker-guided cancer immunotherapy and adoptive cell therapy: a mini review.Frontiers in medicine · 2026Review
- Protein palmitoylation in hematological malignancies: mechanistic links to lipid metabolic reprogramming and therapy resistance.Frontiers in immunology · 2026Review
- In silico generation of synthetic cancer genomes using generative AI.Cell genomics · 2025Article
- Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer.JCO clinical cancer informatics · 2025Article
- Article
- Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study.JMIR medical informatics · 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
29 authors at 9 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSynthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology.
methodsA conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data.
resultsWe generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients.
conclusionSynthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific use and value of real data, thus accelerating precision medicine in hematology and the conduction of clinical trials.
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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.