计算机科学
人工智能
深度学习
机器学习
大数据
数据科学
医学影像学
人工智能应用
周转时间
质量(理念)
集合(抽象数据类型)
数据挖掘
认识论
操作系统
哲学
程序设计语言
作者
James H. Thrall,Xiang Li,Quanzheng Li,Cinthia Cruz,Synho Do,Keith J. Dreyer,James A. Brink
标识
DOI:10.1016/j.jacr.2017.12.026
摘要
Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datasets (“big data”), substantial advances in computing power, and new deep-learning algorithms. Apart from developing new AI methods per se, there are many opportunities and challenges for the imaging community, including the development of a common nomenclature, better ways to share image data, and standards for validating AI program use across different imaging platforms and patient populations. AI surveillance programs may help radiologists prioritize work lists by identifying suspicious or positive cases for early review. AI programs can be used to extract “radiomic” information from images not discernible by visual inspection, potentially increasing the diagnostic and prognostic value derived from image datasets. Predictions have been made that suggest AI will put radiologists out of business. This issue has been overstated, and it is much more likely that radiologists will beneficially incorporate AI methods into their practices. Current limitations in availability of technical expertise and even computing power will be resolved over time and can also be addressed by remote access solutions. Success for AI in imaging will be measured by value created: increased diagnostic certainty, faster turnaround, better outcomes for patients, and better quality of work life for radiologists. AI offers a new and promising set of methods for analyzing image data. Radiologists will explore these new pathways and are likely to play a leading role in medical applications of AI.
科研通智能强力驱动
Strongly Powered by AbleSci AI