Artificial intelligence in oncological radiology A (p)review

Background Artificial intelligence (AI) has the potential to fundamentally change medicine within the coming decades. Radiological imaging is one of the primary fields of its clinical application. Objectives In this article, we summarize previous AI developments with a focus on oncological radiology. Based on selected examples, we derive scenarios for developments in the next 10 years. Materials and methods This work is based on a review of various literature and product databases, publications by regulatory authorities, reports, and press releases. Conclusions The clinical use of AI applications is still in an early stage of development. The large number of research publications shows the potential of the field. Several certified products have already become available to users. However, for a widespread adoption of AI applications in clinical routine, several fundamental prerequisites are still awaited. These include the generation of evidence justifying the use of algorithms through representative clinical studies, adjustments to the framework for approval processes and dedicated education and teaching resources for its users. It is expected that use of AI methods will increase, thus, creating new opportunities for improved diagnostics, therapy, and more efficient workflows.