本研究的目的是验证放射学人工智能 (AI) 解决方案的国家描述性和分析性网格。 DRIM 法国 IA 小组的放射专家专家选择兰德-加州大学洛杉矶分校适当性方法用于本声明文件。该研究由放射学界发起，涉及文献综述、模板开发、专家组选择、专家组会议前调查、数据提取和分析、第二次和最后一次专家组会议以及数据报告七个步骤。专家组由七家软件供应商组成，三个用于使用传统放射学检测骨折，四个用于使用乳房X光检查检测乳腺癌。就总体目标、主要目标、认证标记、集成、结果表达、法证方面和网络安全、性能和科学验证、公司描述和经济细节、临床工作流程中可能的使用场景等各个方面达成了共识，该研究以乳腺癌和骨折为实验指南，验证了由十个项目组成的放射人工智能解决方案的描述性和分析性网格。该网格将帮助放射科医生选择相关且经过验证的人工智能解决方案。网格的进一步发展需要包括其他机构和任务。版权所有 © 2023 Société française de radiologie。由 Elsevier Masson SAS 出版。版权所有。

The purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology.The 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.The 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.The 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.Copyright © 2023 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.