Bridging Industry and Academia: Proceedings from the 2025 Academy Roundtable on AI Implementation in Medical Imaging.

IF 13.2 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Alexander J Towbin, Woojin Kim, Nina Kottler
{"title":"Bridging Industry and Academia: Proceedings from the 2025 Academy Roundtable on AI Implementation in Medical Imaging.","authors":"Alexander J Towbin, Woojin Kim, Nina Kottler","doi":"10.1148/ryai.250671","DOIUrl":null,"url":null,"abstract":"<p><p>Despite rapid advancements in artificial intelligence (AI) for medical imaging, widespread clinical adoption remains limited. In March 2025, the Academy for Radiology & Biomedical Imaging Research convened a cross-sector roundtable to examine operational and structural challenges in AI development and implementation. Researchers, department leaders, government representatives, and industry executives participated in a structured two-stage discussion using the AI lifecycle and a simplified failure modes and effects analysis (sFMEA) framework. In the first stage, attendees examined each phase of the AI lifecycle to identify domains where implementation barriers arise. In the second stage, mixed stakeholder groups applied a qualitative sFMEA approach to analyze process vulnerabilities within those domains and discuss mitigation approaches. This manuscript summarizes the session design, synthesizes key domains, and presents illustrative mitigation approaches across five areas: governance, use cases, implementation, cost, and regulation. The discussion identified recurring challenges related to fragmented priorities, infrastructure constraints, and regulatory complexity, as well as the need for clearer governance structures and more consistent evaluation processes to improve coordination across stakeholders.</p>","PeriodicalId":29787,"journal":{"name":"Radiology-Artificial Intelligence","volume":" ","pages":"e250671"},"PeriodicalIF":13.2000,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology-Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1148/ryai.250671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Despite rapid advancements in artificial intelligence (AI) for medical imaging, widespread clinical adoption remains limited. In March 2025, the Academy for Radiology & Biomedical Imaging Research convened a cross-sector roundtable to examine operational and structural challenges in AI development and implementation. Researchers, department leaders, government representatives, and industry executives participated in a structured two-stage discussion using the AI lifecycle and a simplified failure modes and effects analysis (sFMEA) framework. In the first stage, attendees examined each phase of the AI lifecycle to identify domains where implementation barriers arise. In the second stage, mixed stakeholder groups applied a qualitative sFMEA approach to analyze process vulnerabilities within those domains and discuss mitigation approaches. This manuscript summarizes the session design, synthesizes key domains, and presents illustrative mitigation approaches across five areas: governance, use cases, implementation, cost, and regulation. The discussion identified recurring challenges related to fragmented priorities, infrastructure constraints, and regulatory complexity, as well as the need for clearer governance structures and more consistent evaluation processes to improve coordination across stakeholders.

连接工业和学术界:2025年医学成像中人工智能实施的学术圆桌会议论文集。
尽管人工智能(AI)在医学成像方面取得了迅速进展,但广泛的临床应用仍然有限。2025年3月,放射与生物医学成像研究学院召开了一次跨部门圆桌会议,研究人工智能开发和实施中的运营和结构挑战。研究人员、部门领导、政府代表和行业高管使用人工智能生命周期和简化的故障模式和影响分析(sFMEA)框架参加了结构化的两阶段讨论。在第一阶段,与会者检查了人工智能生命周期的每个阶段,以确定出现实施障碍的领域。在第二阶段,混合利益相关者小组应用定性的sFMEA方法来分析这些领域内的流程脆弱性,并讨论缓解方法。本文总结了会议设计,综合了关键领域,并介绍了五个领域的说明性缓解方法:治理、用例、实现、成本和监管。讨论确定了与分散的优先级、基础设施限制和监管复杂性以及需要更清晰的治理结构和更一致的评估过程以改善利益相关者之间的协调相关的反复出现的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书