A decade has passed since the widely celebrated victory of AlexNet, a convolutional
neural network (CNN) at the 2012 ImageNet classification challenge, which demonstrated
the wide accessibility of GPU-accelerated CNN implementations, and the remarkable
potential of CNNs in accurate image classification (
1
). This impressive performance and the apparent adaptability to medical image classification
problems led to suggestions that expert-based professions relying on image classification
would be imminently entirely supplanted by machines, with bold proposals to disband
radiology residency programs. Following a period of in-depth analysis of the clinical
value of AI in medical imaging and the numerous efforts in supplanting human radiologists
with varying degrees of success and generalizability (
2
), a new more mature paradigm emerged. In this paradigm, humans, and AI are envisioned
working together to redefine and expand the role of medical imaging to support unprecedented
advancements in patient care (
3
). Indeed, the opportunities for fruitful interactions of human and machine seem boundless,
including radiomics-supported “digital biopsy” (
4
), image-based treatment response prediction and plan selection (
5
), and automated image segmentation for improved on-the-fly visualization of patient
anatomy to enable precise personalized interventions (
6
) among numerous others.Abbreviations:
AI (Artificial Intelligence), CNN (Convolutional Neural Network), GPU (Graphics Processing Unit), NIH (National Institutes of Health), NLP (Natural Language Processing), RADS (Reporting, and Data System)To read this article in full you will need to make a payment
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Article Info
Publication History
Published online: March 26, 2022
Accepted:
March 2,
2022
Received in revised form:
March 1,
2022
Received:
March 1,
2022
Identification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
