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Scorecards for Synthetic Medical Data Evaluation and Reporting

2024/06/17 by Ghada Zamzmi, Zamzmi, Ghada, Adarsh Subbaswamy +9 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2406.11143

openalex publication_date 2024/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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