A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop
A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop
复制标题
DOI：
10.1016/j.jacr.2019.04.014
复制
发表时间：
2019-09-01
影响因子：
4.5
通讯作者：
Kandarpa, Krishna
中科院分区：
文献类型：
--
作者：
Allen, Bibb, Jr.;Seltzer, Steven E.;Kandarpa, Krishna
机器学习在医学成像方面的进展在学术机构和工业的研究实验室中都在迅速发展。用于诊断成像的重要人工智能（AI）工具包括用于疾病检测和分类、图像优化、减少辐射和增强工作流程的算法。尽管基础研究进展迅速，但转化为常规临床实践的速度较慢。2018年8月，美国国立卫生研究院在一次公开会议上召集了多个相关利益攸关方，讨论了目前的知识状况、基础设施差距以及更广泛实施的挑战。两份出版物总结了该会议的结论，确定了加速人工智能用于医学成像的基础和转化研究的举措并确定了优先次序。本出版物总结了研讨会上开展的转化研究的关键优先事项，包括：(1)创建结构化的人工智能用例，定义和突出人工智能可能解决的临床挑战；(2)建立鼓励数据共享的方法，用于训练和测试人工智能算法，以促进推广到广泛的临床实践，并减轻意外偏见；(3)建立人工智能算法的验证和性能监控工具，以促进监管审批；(4)制定标准和通用数据元素，以便将人工智能工具无缝集成到现有的临床工作流程中。由此产生的路线图的一个重要目标是在专业协会、行业和政府机构的推动下建立一个生态系统，使执业临床医生和人工智能研究人员之间能够进行强有力的合作，以推进与医学成像相关的基础和转化研究。(C) 2019年由爱思唯尔代表美国放射学会出版
Advances in machine learning in medical imaging are occurring at a rapid pace in research laboratories both at academic institutions and in industry. Important artificial intelligence (AI) tools for diagnostic imaging include algorithms for disease detection and classification, image optimization, radiation reduction, and workflow enhancement. Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower. In August 2018, the National Institutes of Health assembled multiple relevant stakeholders at a public meeting to discuss the current state of knowledge, infrastructure gaps, and challenges to wider implementation. The conclusions of that meeting are summarized in two publications that identify and prioritize initiatives to accelerate foundational and translational research in AI for medical imaging. This publication summarizes key priorities for translational research developed at the workshop including: (1) creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI; (2) establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias; (3) establishing tools for validation and performance monitoring of AI algorithms to facilitate regulatory approval; and (4) developing standards and common data elements for seamless integration of AI tools into existing clinical workflows. An important goal of the resulting road map is to grow an ecosystem, facilitated by professional societies, industry, and government agencies, that will allow robust collaborations between practicing clinicians and AI researchers to advance foundational and translational research relevant to medical imaging. (C) 2019 Published by Elsevier on behalf of American College of Radiology