Artificial Intelligence in Radiology: Methodological Foundations, Clinical Applications, and Emerging Multimodal Models

Abstract

Abstract Artificial intelligence (AI) is increasingly shaping radiology, by performing tasks ranging from image reconstruction and workflow optimization to image analysis, reporting support, and multimodal clinical decision support. This narrative review provides a structured overview of the methodological foundations and clinical evolution of AI in radiology. We first outline the historical development from early rule-based expert systems and classical feature engineering to data-driven machine learning. We then explain the core principles of neural networks, including network depth, nonlinear activation functions, loss-based optimization, and backpropagation, before discussing convolutional neural networks (CNNs) as the dominant architecture for image-based learning in radiology. Particular emphasis is placed on inductive biases such as locality, parameter sharing, and translation equivariance, as well as on encoder–decoder architectures and U-Net-based approaches for segmentation. Subsequently, we discuss attention mechanisms, Transformer architectures, and their role in modeling long-range dependencies in medical imaging. We distinguish Vision Transformers, which process images as token sequences, from vision–language models, which align imaging data with textual information and enable multimodal tasks such as report generation, image–text retrieval, and visual question answering. Recent radiology-specific foundation models and large language models are considered in the context of workflow support, structured reporting, knowledge integration, and emerging multimodal image interpretation. Finally, we address key challenges for clinical translation, including data requirements, bias, generalizability, hallucinations, interpretability, validation, federated learning, and regulatory aspects such as the EU AI Act and the Medical Device Regulation. A basic understanding of these methodological principles is essential for radiologists to critically evaluate current AI systems and to contribute to their safe, transparent, and clinically meaningful implementation.