Scorecards for Synthetic Medical Data Evaluation and Reporting

Autor:	Zamzmi, Ghada, Subbaswamy, Adarsh, Sizikova, Elena, Margerrison, Edward, Delfino, Jana, Badano, Aldo
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computers and Society Computer Science - Databases
Druh dokumentu:	Working Paper
Popis:	The growing utilization of synthetic medical data (SMD) in training and testing AI-driven tools in healthcare necessitates a systematic framework for assessing SMD quality. The current lack of a standardized methodology to evaluate SMD, particularly in terms of its applicability in various medical scenarios, is a significant hindrance to its broader acceptance and utilization in healthcare applications. Here, we outline an evaluation framework designed to meet the unique requirements of medical applications, and introduce the concept of SMD scorecards, which can serve as comprehensive reports that accompany artificially generated datasets. This can help standardize evaluation and enable SMD developers to assess and further enhance the quality of SMDs by identifying areas in need of attention and ensuring that the synthetic data more accurately approximate patient data. Comment: 7 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.11143 Zobrazit plný text záznamu View this record from Arxiv