Summary of Scorecards For Synthetic Medical Data Evaluation and Reporting, by Ghada Zamzmi et al.
Scorecards for Synthetic Medical Data Evaluation and Reporting
by Ghada Zamzmi, Adarsh Subbaswamy, Elena Sizikova, Edward Margerrison, Jana Delfino, Aldo Badano
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an evaluation framework for assessing the quality and applicability of synthetic medical data (SMD) for training and testing artificial intelligence (AI) methods. The proposed framework is designed to meet the unique requirements of medical applications, and includes a comprehensive reporting tool called SMD Card. This card provides a standardized and transparent way to evaluate and report on the quality of SMD, which can benefit developers, users, and regulators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to solve a problem in the use of synthetic medical data for AI models. Right now, there’s no standard way to check if this kind of data is good enough to train or test AI methods. The researchers propose a new way to evaluate synthetic medical data, which they call SMD Card. This card helps developers, users, and people who make rules understand the quality of the synthetic data. It will be helpful for AI models that use this type of data in important submissions. |
Keywords
» Artificial intelligence » Synthetic data
