<p>This paper provides a scientometric study of Generative Adversarial Networks (GANs) in the field of medical imaging, using 2393 peer-reviewed articles published between 2017 and 2024 and indexed in Scopus and Web of Science. Employing performance indicators, co-authorship and collaboration networks, keyword co-occurrence mapping, and thematic evolution analysis, the research maps the intellectual landscape, top contributors, and international collaborations driving this field. The findings show a sharp increase in medical imaging literature based on GANs, with an apex during 2021–2022 followed by a slight decrease, both an indication of citation lag among recent publications and some deflection of interest to other generative approaches like diffusion models and transformers. Regional-wise, the United States and China lead in terms of publication volume and influence, aided by institutions like Johns Hopkins University and Shanghai Jiao Tong University. Thematic clusters highlight super-resolution, segmentation, cross-modality synthesis, and privacy-preserving data generation, reflecting a move away from novelty in algorithms to clinically impactful applications. The field, however, is beset by major challenges such as diminishing citation impact of new papers, absence of comparative clinical evaluation metrics, technical constraints such as mode collapse and instability, and ethical issues involving data ownership rights and patient consent. Looking ahead, the research emphasizes the need for clinically validated GAN architectures, multimodal fusion, privacy-preserving synthetic imaging, and hybrid models that blend GANs with diffusion and transformer models. To prioritize these areas through interdisciplinary research, responsible stewardship, and translational verification will be essential to ensuring that GANs fulfill their revolutionary potential in diagnostic imaging and patient-oriented healthcare.</p>