Research and Analysis of Intelligent Diagnosis Technology in Medical Imaging

Abstract

 As a core basis for clinical diagnosis, medical imaging holds crucial information for disease screening, classification, and treatment efficacy evaluation. However, traditional manual image interpretation faces three major bottlenecks: low efficiency, limited capability, and uneven resource distribution. With the popularization of precision medicine and exponential growth of imaging data, intelligent medical imaging diagnosis has become vital to optimizing the diagnostic process. For example, the ViT-VGG16 hybrid architecture enables collaborative modeling of "local details-global correlations" in small-sample scenarios; BoTNet balances multi-scale complex lesion modeling with hardware compatibility; and the CNN-dictionary representation hybrid builds a "global-local" dual feature system to improve diagnostic accuracy. Due to new diseases and more complex medical needs, this field is evolving, with intelligent technologies addressing challenges like small-sample learning, data privacy, and workflow disconnection. This review compiles relevant literature with two key goals: first, systematically sort out and analyze mainstream intelligent technologies, their principles, and performance; second, examine technical value, significance, dataset characteristics, and propose clinical implementation solutions. These analyses not only guide researchers in technological innovation but also provide evidence-based references for clinicians to promote intelligent technology in frontline practice, while serving as an empirical basis for governments to formulate medical AI development strategies.