J Korean Soc Radiol.  2018 May;78(5):301-308. 10.3348/jksr.2018.78.5.301.

Artificial Intelligence in Medicine: Beginner's Guide

Affiliations
  • 1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. parksh.radiology@gmail.com

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.


MeSH Terms

Artificial Intelligence*
Delivery of Health Care
Diagnosis
Diagnostic Imaging
Leadership
Machine Learning
Patient Care

Figure

  • 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).


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