Applications of artificial intelligence in radiology

Abstract

Artificial intelligence (AI) is increasingly finding its way into routine radiological work. Presentation of the current advances and applications of AI along the entire radiological patient journey. Systematic literature review of established AI techniques and current research projects, with reference to consensus recommendations. The applications of AI in radiology cover awide range, starting with AI-supported scheduling and indications assessment, extending to AI-enhanced image acquisition and reconstruction techniques that have the potential to reduce radiation doses in computed tomography (CT) or acquisition times in magnetic resonance imaging (MRI), while maintaining comparable image quality. These include computer-aided detection and diagnosis, such as fracture recognition or nodule detection. Additionally, methods such as worklist prioritization and structured reporting facilitated by large language models enable arethinking of the reporting process. The use of AI promises to increase the efficiency of all steps of the radiology workflow and an improved diagnostic accuracy. To achieve this, seamless integration into technical workflows and proven evidence of AI systems are necessary. Applications of AI have the potential to profoundly influence the role of radiologists in the future.