## 1 Introduction  
Generative Artificial Intelligence (AI) has recently emerged as a transformative tool for creating new data content--such as text, images, or audio--by modeling patterns observed in training data [1]. Unlike discriminative models, which are typically used for classification and prediction [2], generative models aim to understand the underlying data distribution, enabling them to generate data points that resemble real-world data. This generative characteristic has led to broad practical applications, with one notable area being synthetic data generation, particularly valuable in fields requiring substantial data volume and strict privacy measures. Additionally, with the exponential growth of machine learning applications, there exists increasing dependency on high-quality and high-volume data. Data-driven solutions across many fields rely on rich datasets for effective model training and analysis, yet data limitations frequently hinder progress. In healthcare, for instance, quality data are crucial for solutions in areas such as disease analysis to triage, diagnosis, or treatment [3, 4, 5, 6, 7]. However, data quality issues often stem from limited measurements, while data scarcity and privacy concerns arise due to the sensitive nature of medical records and diagnostic information. To address these issues about data privacy, scarcity, and quality, synthetic data have become a powerful tool. In the medical field, the potential of synthetic data to improve real-world healthcare challenges such as diagnostics, research, and treatment planning is significant [8, 9, 10, 11, 12]. For instance, synthetic data can be deployed to train machine learning models for early cancer detection and prognosis without exposing sensitive patient information. This approach is critical in institutions with limited access to comprehensive datasets. Additionally, synthetic data can simulate patient populations for clinical trials, enabling researchers to evaluate treatment efficacy before conducting expensive and time-intensive trials. This could support the detection of health patterns, disease progression predictions, and, ultimately, clinical decision-making [13]. However, because of the sensitive nature of medical data, taking care of data privacy in the AI field remains essential. In healthcare and other fields involving sensitive information, synthetic data offers a way to overcome challenges of limited availability, privacy restrictions, and class imbalance, all of which can limit the model performance. Synthetic data allow organizations to improve AI models while remaining compliant with privacy regulations by producing large, diverse datasets that reflect the statistical characteristics of real data without exposing personal information. Analysts from organizations like Gartner forecast that synthetic data could surpass real data in AI applications by 2030, as privacy concerns escalate [14]. Therefore, synthetic data are expected to become a critical component in sectors that are highly regulated or data-sensitive. Healthcare data generation also encompasses various data types; different approaches have been developed for synthetic data generation, notably Variational Autoencoders (VAEs) [15], Generative Adversarial Networks (GANs) [16], diffusion models [17; 18], and normalizing flows [19; 20]. These techniques have been extensively studied in the context of healthcare, where data security is paramount, as highlighted in recent reviews [21; 22; 23]. While synthetic data generation for medical images has yielded promising results, our focus here is on tabular data generation, which is critical for medical applications but often underrepresented in generative AI studies. There are three primary approaches to generating synthetic tabular data: partially synthetic data, fully synthetic data, and imputation-based methods (see Figure 1). Partially synthetic methods produce datasets that integrate both real data and generated values, providing privacy benefits while preserving data utility.