A RRA Perspective on AI and Machine Learning Applications in Radiology: From Experimental to Clinically Viable...

This article is the first in a seven-part Radiology Research Alliance (RRA) review series on emerging technologies in radiology. It examines the role of artificial intelligence and machine learning applications across three domains: diagnostic interpretation, workflow optimization, and report generation. Advances in deep learning, multimodal large language models, and natural language processing have delivered improvements in accuracy, efficiency, and reporting quality. Yet important challenges remain, including variable performance, limited generalizability, and barriers to workflow integration. Current evidence shows that artificial intelligence is most effective when used to augment human expertise, with radiologist-AI collaboration producing the strongest outcomes. This review highlights the transition of artificial intelligence from experimental innovation to a clinically viable technology poised to enhance radiology practice when thoughtfully implemented with appropriate oversight.