Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging

Geoff Currie; K. Elizabeth Hawk; Eric Rohren; Alanna Vial; Ran Klein

doi:10.1016/j.jmir.2019.09.005

Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging

Geoff Currie, K. Elizabeth Hawk, Eric Rohren, Alanna Vial, Ran Klein

Research output: Contribution to journal › Comment/debate › peer-review

231 Scopus citations

Abstract

Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.

Original language	English (US)
Pages (from-to)	477-487
Number of pages	11
Journal	Journal of Medical Imaging and Radiation Sciences
Volume	50
Issue number	4
DOIs	https://doi.org/10.1016/j.jmir.2019.09.005
State	Published - Dec 2019

Keywords

Medical imaging
artificial intelligence
artificial neural network
convolutional neural network
deep learning

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging

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10.1016/j.jmir.2019.09.005

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title = "Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging",

abstract = "Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.",

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AU - Hawk, K. Elizabeth

AU - Rohren, Eric

AU - Vial, Alanna

AU - Klein, Ran

PY - 2019/12

Y1 - 2019/12

N2 - Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.

AB - Artificial intelligence (AI) in medical imaging is a potentially disruptive technology. An understanding of the principles and application of radiomics, artificial neural networks, machine learning, and deep learning is an essential foundation to weave design solutions that accommodate ethical and regulatory requirements, and to craft AI-based algorithms that enhance outcomes, quality, and efficiency. Moreover, a more holistic perspective of applications, opportunities, and challenges from a programmatic perspective contributes to ethical and sustainable implementation of AI solutions.

KW - Medical imaging

KW - artificial intelligence

KW - artificial neural network

KW - convolutional neural network

KW - deep learning

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ER -

Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging

Abstract

Keywords

ASJC Scopus subject areas

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