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Abstract Generative artificial intelligence (AI) is increasingly deployed in medical imaging to mitigate data scarcity, enable scalable model development, and simulate disease progression; however, as synthetic data approaches photorealism, it introduces a critical vulnerability: the inability to reliably distinguish synthetic from authentic clinical data.
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