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Synthetic data in healthcare

Accelerate innovation, reduce time-to-data, enable data collaborations and save research costs with synthetic patients records.
Synthetic data for banking

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.
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How can synthetic healthcare data help research and development?

Protecting patients’ privacy and health do not have to be mutually exclusive. Synthetic datasets generated from real data on MOSTLY AI’s synthetic data platform are privacy safe, compliant and offer medical-grade accuracy. Synthetic data generation also helps when not enough data is available or when data is missing or imbalanced, allowing scientists to explore ideas quickly and easily. Generating synthetic patient records for in-silico clinical trials and control studies is the fastest way to get ideas off the ground without endangering patients’ privacy.

According to the European Commission’s Joint Research Centre, synthetic data eliminates the bureaucratic burden of gaining access to sensitive data. Cross-border health data sharing is a vital part of healthcare research and of policy making, where aggregate level data should be replaced with more meaningful synthetic patient data.

Data-driven product development

Accelerate product development and research with privacy-safe, shareable synthetic data assets.

Population health analysis for policy-making

Model and
analyze population health, including disease outbreaks,
demographics, and risk factors.

Data synthesis for data privacy and compliance

PHI can only be accessed by authorized individuals for specific purposes. Secondary use cases and further research is only possible with synthetic data.

Ethical and 
explainable AI

Fair models need fair data input. Data synthesis allows
research teams to explore definitions of fairness and their effects on predictions with fast iterations
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