Development Trends and Future Prospects of Artificial Intelligence in Medical Image Processing

In recent years, the momentum of the development of artificial intelligence has become increasingly strong, and the integration of artificial intelligence and various disciplines has also been applied to all aspects of human life. At the same time, in the medical industry related to human health, research on artificial intelligence + medicine has been carried out for many years. In the medical field, the processing of medical images already accounts for 80%-90% of the medical data sources, playing an extremely important role in the doctor's diagnosis and treatment plan formulation. However, a large amount of medical imaging data such as X-ray imaging, CT, MRI, etc. is handed over to doctors to manually identify disease lesions, which inevitably leads to some errors. In addition, the annual growth rate of the above medical images is roughly 30%, but the annual growth rate of radiologists is only 4%, which makes image analysis doctors face more severe analysis tasks. To address such needs, multiple models that apply deep learning technology to medical image analysis have emerged. This article will focus on analyzing the application of three deep learning models in the field of medical image analysis: convolutional neural networks (CNNs), generative adversarial networks (GANs), U-Net and its variants, and introduce their latest development status, analyze and compare their advantages and disadvantages, analyze the technical challenges that the model will encounter in practical applications, and make reasonable future prospects.