Today's radiologists meet tomorrow's AI: the promises, pitfalls, and unbridled potential

被引:9
|
作者
Ng, Dianwen [1 ]
Du, Hao [1 ]
Yao, Melissa Min-Szu [2 ,3 ]
Kosik, Russell Oliver [3 ]
Chan, Wing P. [2 ,3 ,4 ]
Feng, Mengling [1 ,4 ]
机构
[1] Natl Univ Singapore, Saw Swee Hock Sch Publ Hlth, Natl Univ Hlth Syst, Tahir Fdn Bldg 12 Sci Dr 2,10-01, Singapore 11754, Singapore
[2] Taipei Med Univ, Wan Fang Hosp, Dept Radiol, 1 1 Hsing Long Rd,Sect 3, Taipei, Taiwan
[3] Taipei Med Univ, Coll Med, Sch Med, Dept Radiol, Taipei, Taiwan
[4] Taipei Med Univ, Wan Fang Hosp, Med Innovat Dev Ctr, Taipei, Taiwan
关键词
Artificial intelligence; deep learning; diagnostic imaging; radiologists;
D O I
10.21037/qims-20-1083
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Advances in information technology have improved radiologists' abilities to perform an increasing variety of targeted diagnostic exams. However, due to a growing demand for imaging from an aging population, the number of exams could soon exceed the number of radiologists available to read them. However, artificial intelligence has recently resounding success in several case studies involving the interpretation of radiologic exams. As such, the integration of AI with standard diagnostic imaging practices to revolutionize medical care has been proposed, with the ultimate goal being the replacement of human radiologists with AI 'radiologists'. However, the complexity of medical tasks is often underestimated, and many proponents are oblivious to the limitations of AI algorithms. In this paper, we review the hype surrounding AI in medical imaging and the changing opinions over the years, ultimately describing AI's shortcomings. Nonetheless, we believe that AI has the potential to assist radiologists. Therefore, we discuss ways AI can increase a radiologist's efficiency by integrating it into the standard workflow.
引用
收藏
页码:2775 / 2779
页数:5
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