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Training medical AI algorithms requires large volumes of accurately labeleddatasets, which are difficult to obtain in the real world. Synthetic imagesgenerated from deep generative models can help alleviate the data scarcityproblem, but their effectiveness relies on their fidelity to real-world images.Typically, researchers select synthesis models based on image qualitymeasurements, prioritizing synthetic images that appear realistic. However, ourempirical analysis shows that high-fidelity and visually appealing syntheticimages are not necessarily superior. In fact, we present a case wherelow-fidelity synthetic images outperformed their high-fidelity counterparts indownstream tasks. Our findings highlight the importance of comprehensiveanalysis before incorporating synthetic data into real-world applications. Wehope our results will raise awareness among the research community of the valueof low-fidelity synthetic images in medical AI algorithm training.

The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?