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

Quantitative Imaging in Medicine and Surgery

article

Dianwen Ng¹  Hao Du¹  Melissa Min-Szu Yao²  Russell Oliver Kosik³  Wing P. Chan²  Mengling Feng¹ 

[1] Saw Swee Hock School of Public Health, National University Health System, National University of Singapore;Department of Radiology, Wan Fang Hospital, Taipei Medical University;Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University;Medical Innovation Development Center, Wan Fang Hospital, Taipei Medical University

关键词: Artificial intelligence;    deep learning;    diagnostic imaging;    radiologists;   

DOI  :  10.21037/qims-20-1083

学科分类：外科医学

来源: AME Publications

【 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.

【 授权许可】

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