Rethinking Trust in Synthetic Health Data: Lessons From 7 European Research Initiatives

Area of Science:

• Health Informatics
• Data Science
• Medical Research

Background:

• Synthetic data generation (SDG) is promoted for privacy-preserving health data access and reuse.
• Despite technical advances, real-world adoption of synthetic health data remains limited.
• Challenges include data quality, representativeness, infrastructure, trust, and legal uncertainty.

Purpose of the Study:

• To reflect on the practical operationalization of SDG in European health research.
• To synthesize insights from 7 European research initiatives within the HealthData4EU cluster.
• To identify methodological and governance tensions impacting trust and responsible use.

Main Methods:

• Analysis of experiences from 7 European research initiatives.
• Synthesis of cross-project insights on SDG operationalization.
• Examination of implications for trust and responsible use of synthetic health data.

Main Results:

• Recurring tensions exist between methodological approaches and governance frameworks in SDG.
• Technical optimization alone is insufficient for achieving trustworthy synthetic health data.
• Alignment is needed between evaluation, data stewardship, regulatory clarity, and stakeholder engagement.

Conclusions:

• Trustworthy synthetic health data requires a holistic approach beyond technical solutions.
• Addressing practical challenges is crucial for integrating SDG into European health research ecosystems.
• Sustained stakeholder engagement and regulatory clarity are essential for credible SDG adoption.