## 2 Materials and Methods
### Ethical Considerations for Synthetic Data in Sensitive Domains  
Synthetic data offer significant opportunities to address biases in healthcare datasets and alleviate privacy concerns, as previously discussed. However, their use raises important ethical challenges that require careful consideration, including issues related to fairness, privacy, and potential misuse [44]. These challenges highlight the need for a nuanced understanding of the limitations and implications of synthetic data in sensitive domains such as healthcare [45; 46].  
Fairness remains a central concern when using synthetic data. Despite their potential, synthetic data are not inherently unbiased. The generation process relies on real datasets, which often contain patterns and biases embedded in the original data [47]. Consequently, any bias present in the source data is likely to be replicated, or even amplified, in the synthetic dataset. Efforts to mitigate such biases have led to the development of fairness-aware techniques, which could theoretically be applied to both real and synthetic data to address these disparities [48].  
Privacy considerations also require significant attention. Although synthetic data are often perceived as a privacy-preserving solution, this perception can be misleading. As noted by the Royal Society and The Alan Turing Institute, \"Synthetic data are not automatically private\" [49]. While synthetic datasets may obscure direct identifiers, they are not entirely exempt from privacy risks and must still be handled with caution. The illusion of privacy can lead to complacency, but safeguards must be implemented to prevent potential privacy breaches.  
Transparency and explainability are equally crucial when applying synthetic data generation methods, particularly in healthcare. Clearly documenting the entire process--from algorithm selection and parameter tuning to assumptions and challenges encountered--fosters trust and accountability. This documentation should provide stakeholders with detailed insights into how synthetic datasets were created, including any trade-offs made during the process. Transparency ensures that end-users of synthetic data can understand their limitations and make informed decisions about their use.  
Best practices for responsible synthetic data usage include regular bias assessments to identify and mitigate any disparities [47]. Transparent reporting is also essential, requiring clear labeling of synthetic datasets and explanations of the generation process to ensure users understand the data's provenance and limitations [50]. Additionally, the balance between data utility and privacy protection must be carefully evaluated to maximize the effectiveness and ethical alignment of synthetic data applications.  
It is also crucial to recognize that synthetic data cannot fully replace real-world datasets [49]. By design, synthetic data introduce some level of distortion to ensure privacy or mitigate biases. These distortions, while necessary, may affect the validity of downstream analyses and model training. As such, synthetic data should primarily serve as a complementary resource to accelerate the research pipeline. Ultimately, models or tools intended for real-world deployment must be validated and fine-tuned using real data to ensure their reliability and robustness.