Harnessing Generative Models for Synthetic Medical Data: Balancing Innovation with Ethical and Regulatory Compliance

Abstract

This paper discusses a new frontier in the generation of artificial medical data through generative models and the possible implications of such models to change the landscape of healthcare research and clinical practice. These types of models could be used to overcome data scarcity, privacy and ethical anxieties in medical research by creating realistic, diverse datasets through means of generating. Nevertheless, due to the fast development of these technologies, the ethical and regulatory issues are cause of concern, especially concerning patient consent, model transparency, and privacy of data. The most important point in this article is the ability to balance innovation and necessity of responsibility governing. We explore the uses of generative models in healthcare with the help of case studies and examples and analyze the strengths and weaknesses of these applications, as well as the current research aimed at creating a coherent means to utilize the models in the medical industry ethically and with compliance with the existing regulations. The results indicate that although generative models have tremendous potential, special care should be paid on how they are utilized to make them responsibly and safely applicable in medical practice.