  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.
