Artificial Intelligence in Medicine: Beginner's Guide

Seong Ho Park

doi:10.3348/jksr.2018.78.5.301

Journal List > J Korean Soc Radiol > v.78(5) > 1095535

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Park: Artificial Intelligence in Medicine: Beginner's Guide

Special Report

J Korean Soc Radiol 2018;78(5):301-308.

Published online: 20 January 2018

DOI: https://doi.org/10.3348/jksr.2018.78.5.301

Artificial Intelligence in Medicine: Beginner's Guide

Seong Ho Park, MD^∗

Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

^∗Corresponding author: Seong Ho Park, MD Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea. Tel. 82-2-3010-5984 Fax. 82-2-476-4719 E-mail: parksh.radiology@gmail.com

Received 1 April 2018 Accepted 13 April 2018

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Artificial intelligence is expected to influence clinical practice substantially in the foreseeable future. Despite all the excitement around the technology, it cannot be denied that the application of artificial intelligence in medicine is overhyped. In fact, artificial intelligence for medicine is presently in its infancy, and very few are currently in clinical use. To best leverage the potential of this technology to improve patient care, clinicians need to see beyond the hype, as the guidance and leadership of medical professionals are critical in this matter. To this end, medical professionals must understand the underlying technological basics of artificial intelligence, as well as the methodologies of its proper clinical validation. They should also have an impartial, complete view of the capabilities, pitfalls, and limitations of the technology and its use in healthcare. The present article provides succinct explanations of these matters and suggests further reading materials (peer-reviewed articles and web pages) for medical professionals who are unfamiliar with artificial intelligence.

Keywords: Index terms Artificial Intelligence Machine Learning Medicine Medical Imaging Diagnoses

References

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Fig. 1.

A diagram of artificial neural network consisting of multilayer perceptron. This simple diagram is for a conceptual explanation. When the logistic function is used as the activation function, the connection between all nodes (all x variables) in the input layer and one each node in hidden layer 1 makes a separate logistic function. Therefore, four different logistic functions (h₁ to h₄) marked by different colors (red, green, blue, and black) are created to connect the input layer to hidden layer 1 in this example. Other functions such as the tanh or the ReLU can be used as an activation function. Please see the main text for further explanations. ReLU = rectified linear unit, Tanh = hyperbolic tangent

Fig. 2.

A diagram of convolutional neural network. This simple diagram is for a conceptual explanation. A typical convolutional neural network algorithm contains a much greater number of convolution and pooling steps and layers. Adapted from a background image available on the Internet (14).

Table 1.

Opinions on Artificial Intelligence in Medicine Recently Published in Premier Medical and Scientific Journals

Author (Reference)	Journal, Month, Year	Opinions
Obermeyer (2)	New Engl J Med, September 2017	When you look at all of the enthusiasm and hype around how machine learning will contribute to medicine, I think it's quite striking how little machine learning has contributed to medicine already.
The Lancet (3)	Lancet, December 2017	There is no doubt that AI in health care remains overhyped and at risk of commercial exploitation. Despite the excitement around these sophisticated AI technologies, very few are in clinical use.
		The inherent requirement for large-scale, high-quality, well structured data might ultimately limit the areas in which AI can bring benefits to health care.
Beam and Kohane (4)	JAMA, March 2018	Machine learning is not a magic device that can spin data into gold, though many news releases would imply that it can. Instead, it is a natural extension to traditional statistical approaches.
No authors listed (5)	Nature, March 2018	Many reports are best viewed as analogous to studies showing that a drug kills a pathogen in a Petri dish.
No authors listed (5)	Nature, March 2018	They are not applying the evidence-based approaches that are established in mature fields, such as drug development.
		Many reports of new AI diagnostic tools, for example, go no further than preprints or claims on websites. They haven't undergone peer review, and might never do so.

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