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

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)2775-2779
Number of pages5
JournalQuantitative Imaging in Medicine and Surgery
Volume11
Issue number6
DOIs
Publication statusPublished - Jun 2021

Keywords

  • Artificial intelligence
  • Deep learning
  • Diagnostic imaging
  • Radiologists

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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