Validation and implementation of artificial intelligence in radiology : Quo vadis in 2022?

Abstract

The hype around artificial intelligence (AI) in radiology continues and the number of approved AI tools is growing steadily. Despite the great potential, integration into clinical routine in radiology remains limited. In addition, the large number of individual applications poses achallenge for clinical routine, as individual applications have to be selected for different questions and organ systems, which increases the complexity and time required. This review will discuss the current status of validation and implementation of AI tools in clinical routine, and identify possible approaches for an improved assessment of the generalizability of results of AI tools. A literature search in various literature and product databases as well as publications, position papers, and reports from various stakeholders was conducted for this review. Scientific evidence and independent validation studies are available for only afew commercial AI tools and the generalizability of the results often remains questionable. One challenge is the multitude of offerings for individual, specific application areas by alarge number of manufacturers, making integration into the existing site-specific IT infrastructure more difficult. Furthermore, remuneration for the use of AI tools in clinical routine by health insurance companies in Germany is lacking. But in order for reimbursement to be granted, the clinical utility of new applications must first be proven. Such proof, however, is lacking for most applications.