The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology

Artificial intelligence (AI)-based tools are gradually blending into the clinical neuroradiology practice. Due to increasing complexity and diversity of such AI tools, it is not always obvious for the clinical neuroradiologist to capture the technical specifications of these applications, notably as commercial tools very rarely provide full details. The clinical neuroradiologist is thus confronted with the increasing dilemma to base clinical decisions on the output of AI tools without knowing in detail what is happening inside the black box of those AI applications. This dilemma is aggravated by the fact that currently, no established and generally accepted rules exist concerning best clinical practice and scientific and clinical validation nor for the medico-legal consequences in cases of wrong diagnoses. The current review article provides a practical checklist of essential points, intended to aid the user to identify and double-check necessary aspects, although we are aware that not all this information may be readily available at this stage, even for certified and commercially available AI tools. Furthermore, we therefore suggest that the developers of AI applications provide this information.

Automated summary

AI-based tools are increasingly used in clinical neuroradiology practice, but the technical specifications of these tools are not always clear. Clinical neuroradiologists often have to make clinical decisions based on the output of AI tools without knowing the details of how they work. This review article provides a practical checklist to help users identify and double-check necessary aspects.