Synthetic data generation for tabular health records: A systematic review

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Deep Convolutional Generative Adversarial Networks to Enhance Artificial Intelligence in Healthcare: A Skin Cancer Application

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Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges

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An interpretable machine learning-based model for shear resistance prediction of CFRP-strengthened RC beams using experimental and synthetic dataset

Mohammadi, A., Barros, J. A. O., & Sena-Cruz, J. (2024) · Composite Structures · doi:10.1016/j.compstruct.2024.118632 · Cited by 26 · see citing works

Systematic Review of Generative Modelling Tools and Utility Metrics for Fully Synthetic Tabular Data

Lautrup, A. D., Hyrup, T., Zimek, A., & Schneider–Kamp, P. (2024) · ACM Computing Surveys · doi:10.1145/3704437 · Cited by 26 · see citing works

Improving irregular temporal modeling by integrating synthetic data to the electronic medical record using conditional GANs: a case study of fluid overload prediction in the intensive care unit

Rafiei, A., Rad, M. G., Sikora, A. N., & Kamaleswaran, R. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107749 · Cited by 24 · see citing works

An evaluation of the replicability of analyses using synthetic health data

Emam, K. E., Mosquera, L., Fang, X., & El‐Hussuna, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-57207-7 · Cited by 32 · see citing works

Private pathological assessment via machine learning and homomorphic encryption

Badawi, A. A., & Yusof, M. F. (2024) · BioData Mining · doi:10.1186/s13040-024-00379-9 · Cited by 23 · see citing works

Influence of Optimization Algorithms and Computational Complexity on Concrete Compressive Strength Prediction Machine Learning Models for Concrete Mix Design

Ziółkowski, P. (2025) · Materials · doi:10.3390/ma18061386 · Cited by 23 · see citing works

Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing

Terziyan, V. Y., Malyk, D., Golovianko, M., & Branytskyi, V. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.205 · Cited by 23 · see citing works

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design

Ziółkowski, P. (2023) · Materials · doi:10.3390/ma16175956 · Cited by 22 · see citing works

Exploring Innovative Approaches to Synthetic Tabular Data Generation

Papadaki, E., Vrahatis, A. G., & Kotsiantis, S. B. (2024) · Electronics · doi:10.3390/electronics13101965 · Cited by 21 · see citing works

Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets

Kababji, S. E., Mitsakakis, N., Fang, X., Beltran-Bless, A.-A., Pond, G. R., Vandermeer, L., Radhakrishnan, D., Mosquera, L., Paterson, A. H. G., Shepherd, L. E., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00116 · Cited by 20 · see citing works

Synthetic Data and Its Utility in Pathology and Laboratory Medicine

Pantanowitz, J., Manko, C. D., Pantanowitz, L., & Rashidi, H. H. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102095 · Cited by 20 · see citing works

How good is your synthetic data? SynthRO, a dashboard to evaluate and benchmark synthetic tabular data

Santangelo, G., Nicora, G., Bellazzi, R., & Dagliati, A. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02731-9 · Cited by 19 · see citing works

An Evaluation Framework for Synthetic Data Generation Models

Livieris, I. E., Alimpertis, N., Domalis, G., & Tsakalidis, D. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63219-8_24 · Cited by 21 · see citing works

Synthetic data as external control arms in scarce single-arm clinical trials

Elvatun, S., Knoors, D., Brant, S. B., Jonasson, C., & Nygård, J. F. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000581 · Cited by 18 · see citing works

A systematic review of privacy-preserving techniques for synthetic tabular health data

Hyrup, T., Lautrup, A. D., Zimek, A., & Schneider–Kamp, P. (2025) · Discover Data · doi:10.1007/s44248-025-00022-w · Cited by 20 · see citing works

Enhancing public research on citizen data: An empirical investigation of data synthesis using Statistics New Zealand’s Integrated Data Infrastructure

Wang, A. X., Chukova, S. S., Sporle, A. A., Milne, B. J., Simpson, C. R., & Nguyen, B. P. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103558 · Cited by 18 · see citing works

Generation of a Realistic Synthetic Laryngeal Cancer Cohort for AI Applications

Katalinic, M., Schenk, M., Franke, S., Katalinic, A., Neumuth, T., Dietz, A., Stoehr, M., & Gaebel, J. (2024) · Cancers · doi:10.3390/cancers16030639 · Cited by 18 · see citing works

Challenges and applications in generative AI for clinical tabular data in physiology

Umesh, C., Mahendra, M., Bej, S., Wolkenhauer, O., & Wolfien, M. (2024) · Pflügers Archiv - European Journal of Physiology · doi:10.1007/s00424-024-03024-w · Cited by 19 · see citing works

Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation

Sella, N., Guinot, F., Lagrange, N., Albou, L., Desponds, J., & Isambert, H. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01431-6 · Cited by 17 · see citing works

Evaluation of synthetic electronic health records: A systematic review and experimental assessment

Budu, E., Etminani, K., Soliman, A., & Rognvaldsson, T. S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128253 · Cited by 16 · see citing works

On the Adoption of Modern Technologies to Fight the COVID-19 Pandemic: A Technical Synthesis of Latest Developments

Majeed, A., & Zhang, X. (2023) · COVID · doi:10.3390/covid3010006 · Cited by 16 · see citing works

Tabular transformer generative adversarial network for heterogeneous distribution in healthcare

Kang, H. Y. J., Ko, M., & Ryu, K. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-93077-3 · Cited by 15 · see citing works

Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care

Hahn, W., Schütte, K., Schultz, K., Olaf Wolkenhauer, A., Sedlmayr, M., Schuler, U. S., Eichler, M., Bej, S., & Wolfien, M. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12081278 · Cited by 16 · see citing works

An enhancement of machine learning model performance in disease prediction with synthetic data generation

Kannan, M., Umamaheswari, D., Manimekala, B., Mary, P. S., Savitha, P. M., & Rozario, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-15019-3 · Cited by 14 · see citing works

Generating synthetic multidimensional molecular time series data for machine learning: considerations

An, G. C., & Cockrell, C. (2023) · Frontiers in Systems Biology · doi:10.3389/fsysb.2023.1188009 · Cited by 14 · see citing works

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders

Norris, M. L., Obeid, N., & Emam, K. E. (2024) · International Journal of Eating Disorders · doi:10.1002/eat.24215 · Cited by 13 · see citing works

Self-Supervision Improves Diffusion Models for Tabular Data Imputation

Liu, Y., Ajanthan, T., Husain, H., & Nguyen, V. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679829 · Cited by 13 · see citing works

Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software

Chen, X., Wu, Z., Shi, X., Cho, H., & Mukherjee, B. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf082 · Cited by 12 · see citing works

Methods for Generating and Evaluating Synthetic Longitudinal Patient Data: A Systematic Review

Perkonoja, K., Auranen, K. J., & Virta, J. (2025) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-025-00223-7 · Cited by 16 · see citing works

Moving Conditional GAN Close to Data: Synthetic Tabular Data Generation and Its Experimental Evaluation

Majeed, A., & Hwang, S. O. (2024) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2024.3442534 · Cited by 12 · see citing works

Status of Synthetic Data Generation for Structured Health Data

Emam, K. E. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00071 · Cited by 12 · see citing works

Revolutionizing Healthcare with Generative Artificial Intelligence Technologies

Rashidieranjbar, F., Farhadi, A., & Zamanifar, A. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-76710-4_10 · Cited by 15 · see citing works

Data-centric AI to Improve Early Detection of Mental Illness

Wang, A. X., Chukova, S. S., Simpson, C. R., & Nguyen, B. P. (2023) · IEEE Statistical Signal Processing Workshop (SSP) · doi:10.1109/ssp53291.2023.10207938 · Cited by 11 · see citing works

A Gaussian-based WGAN-GP oversampling approach for solving the class imbalance problem

Zhou, Q., & Sun, B. (2024) · International Journal of Applied Mathematics and Computer Science · doi:10.61822/amcs-2024-0021 · Cited by 10 · see citing works

On the evaluation of synthetic longitudinal electronic health records

Achterberg, J., Haas, M., & Spruit, M. R. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02304-4 · Cited by 12 · see citing works

Feature group tabular transformer: a novel approach to traffic crash modeling and causality analysis

Lares, O., Zhen, H., & Yang, J. J. (2025) · Applied Computing and Intelligence · doi:10.3934/aci.2025003 · Cited by 10 · see citing works

Conditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations

Liu, K., & Altman, R. B. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094844 · Cited by 11 · see citing works

Use of Synthetic Data in Maritime Applications for the Problem of Steam Turbine Exergy Analysis

Šegota, S. B., Mrzljak, V., Anđelić, N., Poljak, I., & Car, Z. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11081595 · Cited by 10 · see citing works

A Text-to-tabular Approach to Generate Synthetic Patient Data using LLMs

Törnqvist, M., Zucker, J., Fauvel, T., Lambert, N., Berthelot, M., & Movschin, A. (2025) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi64645.2025.00011 · Cited by 9 · see citing works

MedWGAN based synthetic dataset generation for Uveitis pathology

Sliman, H., Megdiche, I., Alajramy, L., Taweel, A., Yangui, S., Drira, A., & Lamine, E. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200223 · Cited by 9 · see citing works

Synthetic Subject Generation with Coupled Coherent Time Series Data

Larrea, X., Hernandez, M., Epelde, G., Beristain, A., Molina, C., Alberdi, A., Rankin, D., Bamidis, P. D., & Konstantinidis, E. I. (2022) · International Conference on Time Series and Forecasting (ITISE) · doi:10.3390/engproc2022018007 · Cited by 9 · see citing works

Fed-OLF: Federated Oversampling Learning Framework for Imbalanced Software Defect Prediction Under Privacy Protection

Hu, X., Zheng, M., Zhu, R., Zhang, X., & Jin, Z. G. (2025) · IEEE Transactions on Reliability · doi:10.1109/tr.2024.3524064 · Cited by 8 · see citing works

Explainable evaluation of generative adversarial networks for wearables data augmentation

Narteni, S., Orani, V., Ferrari, E., Verda, D., Cambiaso, E., & Mongelli, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110133 · Cited by 8 · see citing works

Fidelity-agnostic synthetic data generation improves utility while retaining privacy

Achterberg, J., Haas, M., van Dijk, B., & Spruit, M. R. (2025) · Patterns · doi:10.1016/j.patter.2025.101287 · Cited by 8 · see citing works

Survey on Tabular Data Privacy and Synthetic Data Generation in Industry 4.0

Koubeissy, H., Amine, A., Kamradt, M., & Makhoul, A. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06823-5 · Cited by 8 · see citing works

A comprehensive evaluation framework for synthetic medical tabular data generation

Kurakova, A., & Homayouni, H. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104939 · Cited by 8 · see citing works

An explainable dataset linking facial phenotypes and genes to rare genetic diseases

Song, J., He, M., Ren, S., & Shen, B. (2025) · Scientific Data · doi:10.1038/s41597-025-04922-z · Cited by 8 · see citing works

A Paradigm Shift From an Experimental-Based to a Simulation-Based Framework Using Motion-Capture Driven MIMO Radar Data Synthesis

Waqar, S., Muaaz, M., Sigg, S., & Pätzold, M. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3386221 · Cited by 7 · see citing works

Using UMAP for Partially Synthetic Healthcare Tabular Data Generation and Validation

Lázaro, C., & Ángulo, C. (2024) · Sensors · doi:10.3390/s24237843 · Cited by 7 · see citing works

Privacy Vulnerabilities in Marginals-based Synthetic Data

Golob, S., Pentyala, S., Maratkhan, A., & Cock, M. D. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00059 · Cited by 7 · see citing works

Realistic synthetic dataset generation for cyber-physical systems: a performance evaluation

Acquaah, Y. T., & Roy, K. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-07292-8 · Cited by 7 · see citing works

Membership Inference Attacks and Differential Privacy: A Study Within the Context of Generative Models

Galende, B. A., Apellániz, P. A., Parras, J., Zazo, S., & Uribe, S. (2025) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2025.3572244 · Cited by 6 · see citing works

Time-series Anonymization of Tabular Health Data using Generative Adversarial Network

Hashemi, A. S., Etminani, K., Soliman, A., Hamed, O., & Lundström, J. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191367 · Cited by 6 · see citing works

Künstliche Intelligenz und sichere Gesundheitsdatennutzung im Projekt KI-FDZ: Anonymisierung, Synthetisierung und sichere Verarbeitung für Real-World-Daten

Praßer, F., Riedel, N., Wolter, S., Corr, D., & Ludwig, M. (2024) · Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz · doi:10.1007/s00103-023-03823-z · Cited by 6 · see citing works

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

Crouzet, A., Lopez, N., Riss‐Yaw, B., Lepelletier, Y., & Demange, L. (2024) · Molecules · doi:10.3390/molecules29122716 · Cited by 6 · see citing works

Sharing is CAIRing: Characterizing principles and assessing properties of universal privacy evaluation for synthetic tabular data

Hyrup, T., Lautrup, A. D., Zimek, A., & Schneider–Kamp, P. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100608 · Cited by 6 · see citing works

Iterative Application of UMAP-Based Algorithms for Fully Synthetic Healthcare Tabular Data Generation

Lázaro, C., & Ángulo, C. (2024) · Algorithms · doi:10.3390/a17120591 · Cited by 6 · see citing works

Health Synthetic Data to Enable Health Learning System and Innovation: A Scoping Review

Tsao, S., Sharma, K., Noor, H., Forster, A. J., & Chen, H. (2023) · Studies in health technology and informatics · doi:10.3233/shti230063 · Cited by 6 · see citing works

Automated Test Generation for Medical Rules Web Services: A Case Study at the Cancer Registry of Norway

Laaber, C., Yue, T., Ali, S., Schwitalla, T., & Nygård, J. F. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3613882 · Cited by 6 · see citing works

Navigating Tabular Data Synthesis Research Understanding User Needs and Tool Capabilities

Restrepo, M. F. D., Groen, S., Panse, F., & Wingerath, W. (2025) · ACM SIGMOD Record · doi:10.1145/3712311.3712315 · Cited by 5 · see citing works

Generating realistic synthetic tabular data with integrated LLM and diffusion models

Isomura, T., Shimizu, R., & Goto, M. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131371 · Cited by 5 · see citing works

SimSUM – simulated benchmark with structured and unstructured medical records

Rabaey, P., Heytens, S., & Demeester, T. (2025) · Journal of Biomedical Semantics · doi:10.1186/s13326-025-00341-6 · Cited by 6 · see citing works

A Framework for Evaluating Synthetic Electronic Health Records

Budu, E., Soliman, A., Etminani, K., & Rognvaldsson, T. S. (2023) · Studies in health technology and informatics · doi:10.3233/shti230149 · Cited by 5 · see citing works

Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer

Hogenboom, J., Gomes, A. L., Dekker, A. L. A. J., van der Graaf, W. T. A., Husson, O., & Wee, L. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.24.00056 · Cited by 5 · see citing works

Generation of Synthetic Tabular Healthcare Data Using Generative Adversarial Networks

Nik, A., Riegler, M. A., Halvorsen, P., & Storås, A. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-27077-2_34 · Cited by 15 · see citing works

The Generation of Synthetic Healthcare Data Using Deep Neural Networks

Hossein Zadeh Nik, A. (2022) · NORA - Norwegian Open Research Archives

GANs for Tabular Healthcare Data Generation: A Review on Utility and Privacy

Coutinho‐Almeida, J., Rodrigues, P. P., & Cruz-Correia, R. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88942-5_22 · Cited by 16 · see citing works

A systematic review of privacy-preserving techniques for synthetic tabular health data

Hyrup, T., Lautrup, A. D., Zimek, A., & Schneider–Kamp, P. (2025) · Discover Data · doi:10.1007/s44248-025-00022-w · Cited by 20 · see citing works

Synthetic tabular health data generation: a practical comparison between correlation and model-based statistical approach and the conditional generative adversarial network approach

Zhang, Y., & Muller, S. (2026) · Figshare · doi:10.6084/m9.figshare.31926649