Synthetic data addresses data access constraints in healthcare. It allows teams to develop, test, and validate solutions without using identifiable patient data, while still maintaining the structure and characteristics required for analysis.

This whitepaper examines how synthetic data can be applied across AI development and data modernization, with a focus on privacy, data quality, and regulatory requirements. It covers the role of synthetic data in reducing dependency on real-world datasets, its use across model training and testing, approaches to preserve data utility while managing privacy risk, and key considerations for bias, data fidelity, governance, and integration with existing data platforms.

Download the whitepaper to understand the role of synthetic data in AI development and testing while meeting privacy and compliance requirements.