Dear Colleagues,

Over the last decade, synthetic data generation has become increasingly popular after many successful deep generative models were developed to generate realistic artificial data. Synthetic text, images, and videos can be generated using deep generative adversarial networks and diffusion probabilistic models. Synthetic data plays a significant role in overcoming privacy concerns as well as the medical domain’s costly and time-consuming annotation process. In addition, synthetic data can overcome the lack of medical data with pathological findings to train machine learning models. Consequently, deficiency issues in the medical data can be addressed. Furthermore, generating synthetic data is essential for research on simulations and digital twins of the human body.  Therefore, synthetic medical data can help improve the generalizability of machine learning models, improving their clarity and causality while supporting the development of physiological simulations. In this Special Issue, we would like to see novel contributions to synthetic data generation (time series data, image, video, and select types of medical data such as fMRI), novel case studies in the medical domain, and improvements to machine learning algorithms in medicine. Some areas of interest for this Special Issue include:

• Novel techniques for generating synthetic data, including advancements in deep generative models, diffusion probabilistic models, and other techniques.
• Medical case studies using synthetic data: specifically studies that illustrate the use of synthetic medical data in various clinical settings.
• Machine learning algorithm improvements that are specifically designed for medical applications.
• Exploring issues relating to data privacy and security, including perspectives from ethics and law.
• Physiological simulations and digital twins, and the use of synthetic data to develop physiological simulations and digital twins of the human body.
• Novel datasets that either combine real and synthetic data or are completely synthetic.

Dr. Vajira Lasantha Thambawita
Prof. Dr. Michael Alexander Riegler
Guest Editors

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• synthetic medical data
• deep generative models
• synthesis of medical data