Synthetic Data: The key to unlocking AI's potential in healthcare
Published on 18th July 2025 Estimated Reading Time: 2 minutes
The integration of artificial intelligence into healthcare is being hindered by challenges such as data scarcity, privacy concerns and regulatory constraints. Healthcare organisations face difficulties in obtaining sufficient volumes of high-quality, real-world data to train AI models, which can accurately predict outcomes or assist in decision-making.
Synthetic data, defined as algorithmically generated data that mimics real-world data, is emerging as a solution to these challenges. This artificially generated data mirrors the statistical properties of real-world data without containing any sensitive or identifiable information, allowing organisations to sidestep privacy issues and adhere to regulatory requirements.
By generating datasets that preserve statistical relationships and distributions found in real data, synthetic data enables healthcare organisations to train AI models with rich datasets while ensuring sensitive information remains secure. The use of synthetic data can also help address bias and ensure fairness in AI systems by enabling the creation of balanced training sets and allowing for the evaluation of model outputs across different demographic groups.
Furthermore, synthetic data can be generated programmatically, reducing the time spent on data collection and processing and enabling organisations to scale their AI initiatives more efficiently. Ultimately, synthetic data are becoming a critical asset in the development of AI in healthcare, enabling faster development cycles, improving outcomes and driving innovation while maintaining trust and security.