Models and Image Training Issues in the Application of Artificial Intelligence in Radiology

Abstract

This article explores the application of artificial intelligence (AI) in the field of radiology, focusing on the methods, algorithms, and models that enhance medical imaging and diagnostic processes. It examines the role of machine learning, particularly convolutional neural networks (CNN), in improving image recognition, segmentation, and classification. The study highlights the potential of AI in early disease detection, workflow optimization, and reducing diagnostic errors. Challenges such as validation of AI tools and addressing biases in training datasets are also discussed. Practical implementations, including the use of transfer learning and hybrid CNN-RNN models, are presented with an emphasis on their impact on medical imaging quality and efficiency. Key findings demonstrate the transformative potential of AI in radiology while outlining future directions for research and development.