Abstract
With the plethora of machine learning (ML) analyses published in the orthopaedic literature
within the last 5 years, several attempts have been made to enhance our understanding
of what exactly ML means and how it is used. At its most fundamental level, ML comprises
a branch of artificial intelligence that uses algorithms to analyze and learn from
patterns in data without explicit programming or human intervention. On the other
hand, traditional statistics require a user to specifically choose variables of interest
to create a model capable of predicting an outcome, the output of which (1) may be
falsely influenced by the variables chosen to be included by the user and (2) does
not allow for optimization of performance. Early publications have served as succinct
editorials or reviews intended to ease audiences unfamiliar with ML into the complexities
that accompany the subject. Most commonly, the focus of these studies concerns the
terminology and concepts surrounding ML because it is important to understand the
rationale behind performing such studies. Unfortunately, these publications only touch
on the most basic aspects of ML and are too frequently repetitive. Indeed, the conclusion
of these articles reiterate that the potential clinical utility of these algorithms
remains tangential at best in their current form and caution against premature adoption
without external validation. By doing so, our perspective and ability to draw our
own conclusions from these studies have not advanced, and we are left concluding with
each subsequent study that a new algorithm is published for an outcome of interest
that cannot be used until further validation. What readers now need is to regress
to embrace the principles of the scientific method that they have used to critically
assess vast numbers of publications before this wave of newly applied statistical
methodology—a guide to interpret results such that their own conclusions can be drawn.
Level of Evidence
Level V, expert opinion.
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Article info
Publication history
Published online: May 10, 2022
Accepted:
April 19,
2022
Received:
November 10,
2021
Footnotes
See commentary on page 159
The authors report that they have no conflicts of interest in the authorship and publication of this article. Full ICMJE author disclosure forms are available for this article online, as supplementary material.
Identification
Copyright
© 2022 by the Arthroscopy Association of North America
