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Review
. 2021 Jul 1;479(7):1497-1505.
doi: 10.1097/CORR.0000000000001679.

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

Michael P Murphy  1 , Nicholas M Brown
Affiliations
Review

CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

Michael P Murphy et al. Clin Orthop Relat Res. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

The authors certify that neither they, nor any members of their immediate families, have funding or commercial associations (consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article. All ICMJE Conflict of Interest Forms for authors and Clinical Orthopaedics and Related Research® editors and board members are on file with the publication and can be viewed on request.

Figures

Fig. 1.
Fig. 1.
This figure shows definitions of artificial intelligence, machine learning (ML), and deep learning.
Fig. 2.
Fig. 2.
This example of an artificial neural network shows the relationship among input, hidden, and output layers as well as the fluctuating network width and depth. The left side represents a simple artificial neural network, where a single hidden layer is employed. The right side represents a deep artificial neural network with multiple layers. The developer may change the width (number of neurons) in a given layer, the depth (number of layers), and the connections between neurons.
Fig. 3.
Fig. 3.
This figure shows an example of an artificial neural network classifying handwritten digits. The input layer (the handwritten number 8) shows the red-green-blue values for each pixel. In this example, beginning hidden layers may begin to function by identifying patterns in the image, such as vertical lines, horizontal lines, or curves. Later layers will build on this. The final output would be to classify the image to the appropriate digit; in this case, the number 8.
Fig. 4.
Fig. 4.
This graph shows the relationship between the amount of data and algorithm performance of traditional ML algorithms and neural networks of few (shallow) layers and many (deep) layers.
Fig. 5.
Fig. 5.
This graph shows how any exponential relationship will always eventually outperform a polynomial relationship. In this figure, although the polynomial equation has a larger exponent of 5, with its smaller base value of 2, the exponential equation will still eventually outperform the polynomial equation.
See this image and copyright information in PMC

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