Why is synthetic data in healthcare mission-critical?
“The resulting dataset has shown astonishing level of realism (are we looking at the original or the synthetic data?) while maintaining all the privacy test. Resulting data not only can be shared freely, but also can help rebalance under- represented classes in research studies via oversampling, making it the perfect input into machine learning and AI models.” - European Commission’s Joint Research Centre
Data privacy and data scarcity are both pressing issues in healthcare. Life-saving research and innovation is bogged down by data access challenges and suboptimal data granularity. Privacy regulations like HIPAA and GDPR limit access to patient data, making collaborative research and data-intensive machine learning development nearly impossible. Machine learning and AI development projects can’t take off without access to granular health data.