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CoMedBench Evaluates Synthetic Medical Data Utility.

Akanta Das, Al Amin Farhad, Mrinmoy Sarkar Anto, David Rehkopf, Ayin Vala, Tanmoy Sarkar Pias· August 14, 2026 View original

Key takeaways

  • CoMedBench is a new benchmark for evaluating synthetic medical data.
  • It assesses both statistical fidelity and downstream task utility across diverse datasets.
  • High-quality synthetic data can largely preserve critical clinical signals for AI model training.
  • Using synthetic data can mitigate privacy risks while accelerating healthcare AI development.

Who benefits

HealthcarePharmaceuticalsMedTechInsuranceAI Research

Summary

CoMedBench is a new multi-source benchmark that rigorously evaluates the fidelity and downstream utility of synthetic medical data generated by various models across 37 dataset-task pairs, demonstrating that synthetic data can largely preserve critical clinical signals for model development while mitigating privacy risks.

Access to real clinical data is vital for developing healthcare AI, but it's heavily restricted by privacy regulations and data-use agreements. Synthetic data offers a promising alternative, preserving statistical and clinical structures while reducing patient re-identification risks. However, assessing its reliability has been challenging due to fragmented evaluation methods. This research introduces CoMedBench, a comprehensive and reproducible benchmark designed to evaluate synthetic medical data generators. It spans 37 dataset-task pairs across static tabular and temporal ICU time-series data from seven public sources, including major intensive-care databases. The benchmark uses a common clinical-validity framework and a shared training/evaluation engine. CoMedBench assesses both statistical fidelity and the utility of synthetic data for downstream tasks by comparing models trained and tested on real versus synthetic data. Results show that synthetic training data largely preserves the downstream signal: for tabular tasks, the best generator, CoMed-TVAE, retained 97.3% of the real data's AUROC utility. While temporal ICU tasks were harder, CoMed-TVAE still achieved around 95% AUROC utility, demonstrating that high-quality synthetic data can effectively support model development.

Why it matters

Healthcare AI developers, researchers, and data privacy officers can leverage CoMedBench to confidently select and utilize synthetic medical data, accelerating AI development while ensuring patient privacy and maintaining high model performance.

How to implement this in your domain

  1. 1Utilize CoMedBench as a standard for evaluating synthetic data generators for healthcare AI projects.
  2. 2Integrate high-performing synthetic data generators, like CoMed-TVAE, into AI development workflows.
  3. 3Develop internal benchmarks based on CoMedBench's methodology to validate synthetic data utility.
  4. 4Collaborate with data privacy and legal teams to establish guidelines for synthetic data use.
  5. 5Train data scientists on the best practices for generating and evaluating synthetic medical data.

Original post by Akanta Das, Al Amin Farhad, Mrinmoy Sarkar Anto, David Rehkopf, Ayin Vala, Tanmoy Sarkar Pias

"arXiv:2608.12805v1 Announce Type: new Abstract: Access to clinical data is essential for developing reliable healthcare machine learning systems, but direct use of electronic health records is constrained by privacy regulation, institutional review, data-use agreements, and the r…"

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Originally posted by Akanta Das, Al Amin Farhad, Mrinmoy Sarkar Anto, David Rehkopf, Ayin Vala, Tanmoy Sarkar Pias on X · view source

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