Can synthetic data bridge the research gap in rare diseases?
- kenashman
- May 13
- 1 min read
Devdiscourse 20 March 2025
One of the most significant advantages of synthetic data is its potential to enhance AI-driven diagnostics. By generating diverse datasets, researchers can train machine learning models to identify rare genetic markers and improve disease detection accuracy.
Rare diseases are challenging to study and diagnose due to the dearth of available patient data. With small patient populations scattered across the world and strict privacy laws like GDPR and HIPAA, accessing real-world patient data is nearly impossible. This delays diagnosis, clinical trials, and drug development - leaving patients waiting for treatments that may never come.
To address these challenges, Synthetic data - artificial datasets that statistically mimic real-world patient data while preserving privacy - has emerged as a promising solution. A new study "Synthetic data generation: a privacy-preserving approach to accelerate rare disease research" published in Frontiers in Digital Health" discusses in depth the potential of synthetic data to revolutionise rare disease research.
