What the radiologist should know about artificial intelligence – an ESR white paper

TLDR

Artificial intelligence is increasingly applied in radiology, offering potential benefits beyond lesion detection and characterization while raising ethical and professional concerns. The paper reviews the foundations of AI use in radiology, examines its immediate ethical and professional effects, and explores possible future developments. The authors describe how AI can be applied through radiomics, imaging biobanks, and optimization tools to enhance image analysis, personalize protocols, monitor dose, and streamline reporting by linking textual, visual, and quantitative data. AI integrated with clinical decision support improves decision‑making and optimizes clinical and radiological workflows.

Abstract

This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution.Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient’s protocol, tracking the patient’s dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow.