mlsanityData to 14 Oct 2026

Scorecards for Synthetic Medical Data Evaluation and Reporting

arXiv 2406.11143 ↗First posted 17 Jun 2024cs.AI cs.CY cs.DBcited by 4

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Abstract (arXiv)

Although interest in synthetic medical data (SMD) for training and testing AI methods is growing, the absence of a standardized framework to evaluate its quality and applicability hinders its wider adoption. Here, we outline an evaluation framework designed to meet the unique requirements of medical applications, and introduce SMD Card, which can serve as comprehensive reports that accompany artificially generated datasets. This card provides a transparent and standardized framework for evaluating and reporting the quality of synthetic data, which can benefit SMD developers, users, and regulators, particularly for AI models using SMD in regulatory submissions.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 13 of its 29 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

13 of 29 references.

References · 13

  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,242
  2. 2018Model Cards for Model Reportingcited by 1,080
  3. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  4. 2022The Vendi Score: A Diversity Evaluation Metric for Machine Learningcited by 311
  5. 2018Pros and Cons of GAN Evaluation Measurescited by 224
  6. 2021Pros and Cons of GAN Evaluation Measures: New Developmentscited by 123
  7. 2024Detecting and Evaluating Medical Hallucinations in Large Vision Language Modelscited by 57
  8. 2020Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluationcited by 36
  9. 2022Healthsheet: Development of a Transparency Artifact for Health Datasetscited by 23
  10. 2023Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Designcited by 23
  11. 2023Constraining Generative Models for Engineering Design with Negative Datacited by 11
  12. 2023Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial contextcited by 7
1 more
  1. 2024Synthetic Data in Radiological Imaging: Current State and Future Outlookcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.