Artificial intelligence in radiology: a narrative review of current methods, clinical impact, and future directions

Abstract Objectives This review synthesizes current artificial intelligence (AI) methodologies and evaluates their clinical impact in diagnostic radiology. As AI tools increasingly enter clinical workflows, understanding their performance, limitations, and barriers to adoption is critical. Methods This review was conducted to provide a focused synthesis of recent advances in artificial intelligence (AI) as applied to diagnostic radiology. Relevant literature was identified through searches of PubMed, Scopus, and Google Scholar, covering publications from 2018 to 2025, using combinations of “artificial intelligence,” “machine learning,” “deep learning,” “radiology,” “medical imaging,” “workflow,” “ethics,” and “regulation.” Additional sources were located by screening reference lists of key papers and reviews. Articles were included if they explored clinical, technical, or ethical dimensions of AI within radiology, with emphasis on convolutional neural networks (CNNs), vision–language models (VLMs), workflow optimization, bias, or regulatory oversi