Journal ArticleDOI
Deep Learning-A Technology With the Potential to Transform Health Care.
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TLDR
The purpose of this Viewpoint is to give health care professionals an intuitive understanding of the technology underlying deep learning, used on billions of digital devices for complex tasks such as speech recognition, image interpretation, and language translation.Abstract:
Widespread application of artificial intelligence in health care has been anticipated for half a century. For most of that time, the dominant approach to artificial intelligence was inspired by logic: researchers assumed that the essence of intelligence was manipulating symbolic expressions, using rules of inference. This approach produced expert systems and graphical models that attempted to automate the reasoning processes of experts. In the last decade, however, a radically different approach to artificial intelligence, called deep learning, has produced major breakthroughs and is now used on billions of digital devices for complex tasks such as speech recognition, image interpretation, and language translation. The purpose of this Viewpoint is to give health care professionals an intuitive understanding of the technology underlying deep learning. In an accompanying Viewpoint, Naylor1 outlines some of the factors propelling adoption of this technology in medicine and health care.read more
Citations
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Journal ArticleDOI
Applications of machine learning in drug discovery and development.
Jessica Vamathevan,Dominic Clark,Paul Czodrowski,Ian Dunham,Edgardo Ferran,George Lee,Bin Li,Anant Madabhushi,Anant Madabhushi,Parantu K. Shah,Michaela Spitzer,Shanrong Zhao +11 more
TL;DR: The most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development are discussed and major hurdles in the field are highlighted.
Journal ArticleDOI
An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: In this article, the authors provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis, and provide a starting point for people interested in experimenting and perhaps contributing to the field of machine learning for medical imaging.
Journal ArticleDOI
An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: This paper indicates how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction, and provides a starting point for people interested in experimenting and contributing to the field of deep learning for medical imaging.
Journal ArticleDOI
The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database
TL;DR: The first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA is launched, which aims to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device isAI/ML based or not.
Journal ArticleDOI
Ensuring Fairness in Machine Learning to Advance Health Equity.
TL;DR: The mechanisms by which a model's design, data, and deployment may lead to disparities are described; how different approaches to distributive justice in machine learning can advance health equity are explained; and what contexts are more appropriate for different equity approaches inMachine learning.
References
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Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva,Brett Kuprel,Roberto A. Novoa,Justin M. Ko,Susan M. Swetter,Susan M. Swetter,Helen M. Blau,Sebastian Thrun +7 more
TL;DR: This work demonstrates an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists, trained end-to-end from images directly, using only pixels and disease labels as inputs.