TY - JOUR
T1 - Today’s radiologists meet tomorrow’s AI
T2 - The promises, pitfalls, and unbridled potential
AU - Ng, Dianwen
AU - Du, Hao
AU - Yao, Melissa Min Szu
AU - Kosik, Russell Oliver
AU - Chan, Wing P.
AU - Feng, Mengling
N1 - Funding Information:
Funding: This study was partially supported by the Singapore Health Service Research Grant HSRG-OC17nov004, and Taipei Medical University (High Education SPROUT Project “Translating Innovation and Integration Research Grant”) DP2-109-21121-01-A-04-01.
Publisher Copyright:
© Quantitative Imaging in Medicine and Surgery. All rights reserved.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Deep learning
KW - Diagnostic imaging
KW - Radiologists
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U2 - 10.21037/qims-20-1083
DO - 10.21037/qims-20-1083
M3 - Article
AN - SCOPUS:85104676469
SN - 2223-4292
VL - 11
SP - 2775
EP - 2779
JO - Quantitative Imaging in Medicine and Surgery
JF - Quantitative Imaging in Medicine and Surgery
IS - 6
ER -