  Training medical AI algorithms requires large volumes of accurately labeled
datasets, which are difficult to obtain in the real world. Synthetic images
generated from deep generative models can help alleviate the data scarcity
problem, but their effectiveness relies on their fidelity to real-world images.
Typically, researchers select synthesis models based on image quality
measurements, prioritizing synthetic images that appear realistic. However, our
empirical analysis shows that high-fidelity and visually appealing synthetic
images are not necessarily superior. In fact, we present a case where
low-fidelity synthetic images outperformed their high-fidelity counterparts in
downstream tasks. Our findings highlight the importance of comprehensive
analysis before incorporating synthetic data into real-world applications. We
hope our results will raise awareness among the research community of the value
of low-fidelity synthetic images in medical AI algorithm training.
