Generative Transport Models in Medical Imaging: A Unified Survey

Generative transport models learn to move samples from one distribution to another, for example from a patient's magnetic resonance image to a computed tomography (CT) scan or from noise to an image. Recent ones, such as flow and bridge models, are used widely in medical imaging, and most of these studies report faster sampling or higher image scores than diffusion. In medicine these measures are incomplete; a synthetic CT scan, for example, must also keep the patient's anatomy and support dose calculation. We therefore review 383 studies of flow and bridge models published from 2023 to 2026, identified by a systematic search, with comparison samples of 55 diffusion and 17 normalizing-flow studies, and compare them across clinical tasks on model design and evaluation endpoints. We find that a model's medical value is shaped by the path the patient's information takes to the output, more than by whether the transport is a diffusion, flow or bridge variant. We present these models as settings of one transport viewpoint with six design axes. We then review four task families, reconstruction, translation, generation and analysis, and distil fifteen observations on the information a model receives and on how well its metrics and comparisons support its claims. For each clinical use we identify the endpoint it requires, which conventional metrics do not replace, together with the datasets and sampling costs a fair comparison needs, and give a procedure for choosing and testing a model. A continually updated reading list is maintained at https://github.com/chqwer2/Awesome-Flow-Based-Medical-Imaging.