ArticleNPJ digital medicine2024
Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis 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
32 citing papers in PubMed, 1 synthesis or guideline 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
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Next-generation synthetic trials in hematology with generative artificial intelligence.Leukemia · 2026Review
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- Real-time prediction of trauma-induced coagulopathy using an inverted transformer (trauma-former): a methodological feasibility and simulation study based on the ADEMP framework.BMC medical research methodology · 2026Article
- A novel digital twin strategy to examine the implications of randomized clinical trials for real-world populations.NPJ digital medicine · 2026Article
- Unconditional latent diffusion models memorize patient imaging data.Nature biomedical engineering · 2026Article
- Privacy and fidelity: The promise of synthetic data in hepatology research.Hepatology (Baltimore, Md.) · 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
- GAN-enhanced digital twins for biomarker-guided cancer immunotherapy and adoptive cell therapy: a mini review.Frontiers in medicine · 2026Review
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Article
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 2025Article
- Article
- Synthetic Tabular Data Generation Under Horizontal Federated Learning Environments in Acute Myeloid Leukemia: Case-Based Simulation Study.JMIR medical informatics · 2025Article
- Review
- Artificial Intelligence-Enabled Clinical Trials in Inflammatory Bowel Disease: Automating and Enhancing Disease Assessment and Study Management.Gastroenterology · 2025Review
- Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.Journal of clinical medicine · 2025Review
- Challenges and applications in generative AI for clinical tabular data in physiology.Pflugers Archiv : European journal of physiology · 2025Review
- Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study.Journal of medical Internet research · 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
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence - CTAB-GAN+ and normalizing flows (NFlow) - to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patient outcomes yielding high performance scores regarding fidelity and usability of both synthetic cohorts (n = 1606 each). Survival analysis demonstrated close resemblance of survival curves between original and synthetic cohorts. Inter-variable relationships were preserved in univariable outcome analysis enabling explorative analysis in our synthetic data. Additionally, training sample privacy is safeguarded mitigating possible patient re-identification, which we quantified using Hamming distances. We provide not only a proof-of-concept for synthetic data generation in multimodal clinical data for rare diseases, but also full public access to synthetic data sets to foster further research.
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.