<p>Generative artificial intelligence is changing how medical imaging is produced and interpreted, mainly because it can create, repair, or refine visual data with far higher detail than earlier tools. Systems built on generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models now produce anatomically consistent images that support faster MRI workflows, reduce noise in low-dose CT, allow one modality to be translated into another, and help create synthetic datasets for conditions that rarely appear in routine practice. Some groups have started tying these models to biological or physical constraints, which has made their behavior easier to interpret and, in some cases, more acceptable to clinicians who rely on stable reasoning when evaluating disease patterns or biomarker candidates. But the path to clinical use is still messy. Pathology-level accuracy isn’t guaranteed, bias can creep in quickly, and hallucinated structures remain a real concern. Bringing these systems into hospitals requires stronger validation, clearer regulatory expectations, and technical setups that don’t break existing workflows; it also depends on honest communication about uncertainty. None of this happens without long-term cooperation among technical teams, clinicians, and policy groups. With the right oversight, GANs could shift medical imaging from a field limited by data scarcity to one that supports predictive modeling for precision medicine. This review steps away from a purely technical catalog and instead examines how governance shapes translation, arguing that the main barrier now is the lack of clinically aligned, verifiable, and controllable systems.</p>