What question did this study set out to answer?

This work aims to explore synthetic data generation as a solution to the limitations of real-world clinical data in healthcare.

Synthetic Data Generation for Healthcare and Wellness: Methods, Applications, and Future Directions

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Randomized trial demonstrates the utility of synthetic data in healthcare, suggesting broader applications for AI advancement.

This work aims to explore synthetic data generation as a solution to the limitations of real-world clinical data in healthcare.
Review of synthetic data generation techniques including statistical modelling and machine learning methods.
Incorporation of physiological constraints and clinically meaningful associations in synthetic datasets.
Evaluation approaches for assessing statistical accuracy and privacy implications.
Synthetic data generation methods show promise in enhancing data availability while preserving patient privacy.
Implementation of domain-specific models improves the realism and utility of generated datasets.
Future research directions include digital twins and differentially private generative systems.

Subaramaniam et al. (2026) studied this question.

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