ReviewRadiology2024
Generating Synthetic Data for Medical Imaging.
Abstract readReview
In one paragraph
Review in Radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
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
42 citing papers in PubMed.
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Microbial Colony Image Enhancement Algorithm Based on Composite Adversarial D-DCGAN Neural Network.Microorganisms · 2026Article
- Ethical considerations in generative artificial intelligence for synthetic data generation in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Ultrasound-Based Tomographic Imaging Reconstruction and Synthesis Methods: a Scoping Review.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence as medical device in radiology in 2025: the regulatory scenario in the EU, USA, and China.European radiology · 2026Review
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- From pixels to precision: a narrative review of AI-driven 3D morphological analysis and digital twinning in alveolar cleft management.Translational pediatrics · 2026Review
- Why Radiomics Rarely Reaches the Clinic: Reproducibility, Validation, and Evidence Gap-A Critical Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning-based generation of synthetic multiphasic MRI in hepatocellular carcinoma and cirrhosis.JHEP reports : innovation in hepatology · 2026Article
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- 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
- Synthetic Implant Migration Generation for Accuracy and Precision Evaluation of AI-Based CT-RSA in Total Hip Arthroplasty.Diagnostics (Basel, Switzerland) · 2026Article
- From Pixel Understanding to Semantic Insight: Intelligent Detection in Sensor-Driven Perception Systems.Sensors (Basel, Switzerland) · 2026Review
- Biologically Inspired Medical Multi-Modal Dataset Distillation via Contrast-Aware Alignment and Memory Compression.Biomimetics (Basel, Switzerland) · 2026Article
- Artificial intelligence-generated synthetic data for cancer research and clinical trials.Nature reviews. Cancer · 2026Review
- Synthetic cervical spine radiographs: expert validation and transfer learning for low-data settings.BMC medical informatics and decision making · 2026Article
- Deep learning detection of retinitis pigmentosa inheritance forms through synthetic data expansion of a rare disease dataset.Scientific reports · 2026Article
- Review
- Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.ESMO real world data and digital oncology · 2026Review
- An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.NAR genomics and bioinformatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Lennart R KoetzierFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).
Jie WuFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).
Domenico MastrodicasaFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).ORCID 0000-0001-8227-0757
Aline LutzFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).
Matthew ChungFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).
W Adam KoszekFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).
Jayanth PratapFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).ORCID 0009-0001-4920-2709
Akshay S ChaudhariFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).ORCID 0000-0002-3667-6796
Pranav RajpurkarFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).
Matthew P LungrenFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).
Martin J WilleminkFrom the Delft University of Technology, Delft, the Netherlands (L.R.K.); Segmed, 3790 El Camino Real #810, Palo Alto, CA 94306 (J.W., A.L., M.C., W.A.K., J.P., M.J.W.); Department of Radiology, University of Washington, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core, Seattle, Wash (D.M.); Harvard University, Cambridge, Mass (J.P.); Department of Radiology, Stanford University School of Medicine, Palo Alto, Calif (A.S.C.); Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, Calif (A.S.C.); Department of Biomedical Informatics, Harvard Medical School, Boston, Mass (P.R.); Microsoft, Redmond, Wash (M.P.L.); and Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (M.P.L.).ORCID 0000-0002-6991-6557
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) models for medical imaging tasks, such as classification or segmentation, require large and diverse datasets of images. However, due to privacy and ethical issues, as well as data sharing infrastructure barriers, these datasets are scarce and difficult to assemble. Synthetic medical imaging data generated by AI from existing data could address this challenge by augmenting and anonymizing real imaging data. In addition, synthetic data enable new applications, including modality translation, contrast synthesis, and professional training for radiologists. However, the use of synthetic data also poses technical and ethical challenges. These challenges include ensuring the realism and diversity of the synthesized images while keeping data unidentifiable, evaluating the performance and generalizability of models trained on synthetic data, and high computational costs. Since existing regulations are not sufficient to guarantee the safe and ethical use of synthetic images, it becomes evident that updated laws and more rigorous oversight are needed. Regulatory bodies, physicians, and AI developers should collaborate to develop, maintain, and continually refine best practices for synthetic data. This review aims to provide an overview of the current knowledge of synthetic data in medical imaging and highlights current key challenges in the field to guide future research and development.
Indexed as
Artificial IntelligenceDiagnostic ImagingHumans
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