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

Dianwen Ng, Hao Du, Melissa Min Szu Yao, Russell Oliver Kosik, Wing P. Chan, Mengling Feng

研究成果: 雜誌貢獻文章同行評審

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.

原文英語
頁(從 - 到)2775-2779
頁數5
期刊Quantitative Imaging in Medicine and Surgery
11
發行號6
DOIs
出版狀態已發佈 - 六月 2021

ASJC Scopus subject areas

  • 放射學、核子醫學和影像學

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