Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records
arXiv:2606.08903v1 Announce Type: new Abstract: Synthetic healthcare data are widely proposed as privacy-preserving substitutes for real patient data, yet their evaluation remains dominated by statistical similarity and predictive performance that do not reflect clinical validity. We introduce a multi-dimensional evaluation framework grounded in epidemiology, assessing descriptive fidelity, clinical utility, and structural validity, corresponding to descriptive, predictive, and causal questions. We evaluate four representative generative paradigms - GAN-based, VAE-boosted, diffusion-based, and
The proliferation of generative AI models for healthcare data, coupled with growing concerns about data privacy and the need for robust evaluation, makes this proposed framework timely.
A standardized, clinically relevant evaluation framework for synthetic medical data will significantly impact the reliability and adoption of AI in healthcare, moving beyond purely statistical metrics.
The focus for evaluating synthetic electronic medical records will shift from purely statistical resemblance to clinical utility and structural validity, impacting development and regulatory pathways.
- · Healthcare AI developers
- · Medical researchers
- · Patients (indirectly through better data security)
- · Generative AI models with poor clinical validity
- · Developers relying solely on statistical evaluation metrics
Improved trust and accelerated adoption of synthetic data in healthcare research and development.
New standards and potential regulatory guidelines emerge for the clinical validation of AI-generated medical data.
The development of a new niche industry focused on clinical validity testing and certification for synthetic healthcare data.
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