AI-Driven Synthetic Health Data Generation for Secure Cloud-Based System Testing and Data Augmentation
Keywords:
Synthetic Health Data Generation, Differential Privacy, Generative Adversarial Networks, Healthcare Machine Learning, Cloud-Based System TestingAbstract
The healthcare industry is faced with the twofold challenge of using massive stores of medicalinformation to spur innovation, and protecting patient privacy against the growing number of privacythreats and increasingly complex regulatory policies. The creation of synthetic health data using state-of the-art artificial intelligence models
References
Jordan Zheng Ting Sim, et al., "Machine learning in medicine: what clinicians should know," PubMed Central, 2021. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC10071847/


