Large scale models in radiology: revolutionizing the future of medical imaging

Abstract

In the domain of medical image analysis, there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly due to their outstanding performance. This review article seeks to concisely explore the historical evolution, specific applications, and training methodologies associated with these large models considering their current prominence in medical image analysis. Moreover, we delve into the prevailing challenges and prospective opportunities related to the utilization of large models in the context of medical image analysis. Through a comprehensive analysis of these substantial models, this study aspires to provide valuable insights and guidance to researchers in the field of radiology, fostering further advances and optimizations in their incorporation into medical image analysis practices, in accordance with the submission requirements.