Abstract
PurposeThe purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology. Materials and methodsThe RAND-UCLA Appropriateness Method was chosen by expert radiologists from the DRIM France IA group for this statement paper. The study, initiated by the radiology community, involved seven steps including literature review, template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting. ResultsThe panel consisted of seven software vendors, three for bone fracture detection using conventional radiology and four for breast cancer detection using mammography. A consensus was reached on various aspects, including general target, main objective, certification marking, integration, expression of results, forensic aspects and cybersecurity, performance and scientific validation, description of the company and economic details, possible usage scenarios in the clinical workflow, database, specific objectives and targets of the AI tool. ConclusionThe study validates a descriptive and analytical grid for radiological AI solutions consisting of ten items, using breast cancer and bone fracture as an experimental guide. This grid would assist radiologists in selecting relevant and validated AI solutions. Further developments of the grid are needed to include other organs and tasks.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
