Citation

Subaramaniam, Kasthuri and Palaniappan, Sellappan and Baker, Oras and Bui, Ngoc Dung and Sumit, Dhariwal (2026) Synthetic Data Generation for Healthcare and Wellness: Methods, Applications, and Future Directions. Journal of Informatics and Web Engineering, 5 (2). p. 343. ISSN 2821-370X

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Abstract

Artificial intelligence in healthcare relies heavily upon the availability of high-quality datasets, but very rigid privacy regimes, institutional silos, ethical issues, and heterogeneous data still serve to limit the availability of real-world clinical data. To combat these limitations, you focus on synthetic data generation as a privacy-preserving and scalable alternative to traditional data generation for healthcare and wellness studies. More relevant is a work on a broad and clear framework of synthesis methods based on statistical modelling, rule-based generation and domain-specific clinical logic, in contrast to recent studies that mainly target sophisticated methods for the deep learning architectures. The paper reviews the major synthetic data generation approaches like statistical distributions, machine learning techniques, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), diffusion models, and hybrid methods. Furthermore, the paper demonstrates how physiological constraints, time structures, prevalence modelling, and clinically meaningful associations could be incorporated into synthetic datasets in a range of healthcare fields including clinical risk prediction, wearable analytics, mental health text generation, genomics, epidemiological modeling and medical imaging. To ensure reproducibility and accessibility for both research and practice, practical Python-based examples along with domain-aware probabilistic models are provided. Further, it discusses evaluation approaches for evaluating statistical accuracy, downstream utility, and privacy in the process, and stresses their trade-offs on realism, technical efficiency, and disclosure potential. Future avenues for research are also presented, (e.g., digital twins, multimodal patient simulation, long-term disease progression modelling, differentially private generative systems). This work would be a theoretical basis as well as a practical guide for researchers, clinicians, and educators striving to create safe, transparent, and trustworthy synthetic healthcare data for advance of healthcare artificial intelligence.

Item Type:	Article
Uncontrolled Keywords:	Synthetic Data, Healthcare Artificial Intelligence, Privacy Preservation, Generative Models, Data Augmentation
Subjects:	R Medicine > R Medicine (General) > R855-855.5 Medical technology
Divisions:	Others
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	09 Jul 2026 03:51
Last Modified:	09 Jul 2026 03:51
URII:	http://shdl.mmu.edu.my/id/eprint/16344

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