Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success
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DOI：
10.1016/j.jacr.2017.12.026
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发表时间：
2018-03-01
影响因子：
4.5
通讯作者：
Brink, James
中科院分区：
文献类型：
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作者：
Thrall, James H.;Li, Xiang;Brink, James
在大型数据集（“大数据”）的可用性、计算能力的实质性进步和新的深度学习算法的推动下，全球对人工智能（AI）应用（包括成像）的兴趣很高，而且增长迅速。除了开发新的人工智能方法本身，成像社区还面临许多机遇和挑战，包括开发通用的命名法，更好地共享图像数据的方法，以及验证人工智能程序在不同成像平台和患者群体中的使用的标准。人工智能监测程序可以帮助放射科医生通过识别可疑或阳性病例进行早期审查来确定工作清单的优先顺序。人工智能程序可用于从视觉检查无法识别的图像中提取“放射学”信息，从而潜在地提高图像数据集的诊断和预后价值。有人预测，人工智能将使放射科医生失业。这个问题被夸大了，放射科医生更有可能将人工智能方法纳入他们的实践中。目前在技术专门知识的可用性甚至计算能力方面的限制将随着时间的推移而得到解决，也可以通过远程访问解决方案加以解决。人工智能在成像领域的成功将通过创造的价值来衡量：提高诊断的确定性、更快的周转速度、更好的患者治疗结果，以及更好的放射科医生的工作生活质量。人工智能为分析图像数据提供了一套新的、有前途的方法。放射科医生将探索这些新的途径，并可能在人工智能的医疗应用中发挥主导作用。
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.