Recent progress in artificial intelligence and machine learning has led to the growth of
research in every aspect of life including the health care domain. However, privacy risks and …

Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …

Background Machine learning (ML) has made a significant impact in medicine and cancer
research; however, its impact in these areas has been undeniably slower and more limited …

Synthetic health data have the potential to mitigate privacy concerns in supporting
biomedical research and healthcare applications. Modern approaches for data generation …

Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving
offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However …

Background: Privacy restrictions limit access to protected patient-derived health information
for research purposes. Consequently, data anonymization is required to allow researchers …

Synthetic data generation has emerged as a promising method to protect patient privacy
while sharing individual-level health data. Intuitively, sharing synthetic data should reduce …

Artificial intelligence (AI), notably Generative Adversarial Networks, has the potential to
transform medical and patient education. Leveraging GANs in medical fields, especially …

Background There has been growing interest in data synthesis for enabling the sharing of
data for secondary analysis; however, there is a need for a comprehensive privacy risk …

Getting access to administrative health data for research purposes is a difficult and time-
consuming process due to increasingly demanding privacy regulations. An alternative …