Daphné Guenoun , Marc Zins , Pierre Champsaur , Isabelle Thomassin-Naggara , DRIM France AI Study Group
{"title":"法国放射性人工智能解决方案评估社区网格（DRIM法国人工智能倡议）。","authors":"Daphné Guenoun , Marc Zins , Pierre Champsaur , Isabelle Thomassin-Naggara , DRIM France AI Study Group","doi":"10.1016/j.diii.2023.09.002","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>The purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology.</p></div><div><h3>Materials and methods</h3><p><span>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, </span>template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting.</p></div><div><h3>Results</h3><p>The panel consisted of seven software vendors, three for bone fracture detection using conventional radiology<span> 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.</span></p></div><div><h3>Conclusion</h3><p>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.</p></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)\",\"authors\":\"Daphné Guenoun , Marc Zins , Pierre Champsaur , Isabelle Thomassin-Naggara , DRIM France AI Study Group\",\"doi\":\"10.1016/j.diii.2023.09.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>The purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology.</p></div><div><h3>Materials and methods</h3><p><span>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, </span>template development, panel selection, pre-panel meeting survey, data extraction and analysis, second and final panel meeting, and data reporting.</p></div><div><h3>Results</h3><p>The panel consisted of seven software vendors, three for bone fracture detection using conventional radiology<span> 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.</span></p></div><div><h3>Conclusion</h3><p>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.</p></div>\",\"PeriodicalId\":48656,\"journal\":{\"name\":\"Diagnostic and Interventional Imaging\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diagnostic and Interventional Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211568423001766\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostic and Interventional Imaging","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211568423001766","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
French community grid for the evaluation of radiological artificial intelligence solutions (DRIM France Artificial Intelligence Initiative)
Purpose
The purpose of this study was to validate a national descriptive and analytical grid for artificial intelligence (AI) solutions in radiology.
Materials and methods
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.
Results
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.
Conclusion
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.
期刊介绍：
Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English.
Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
