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Review
. 2020 Jan 13;54(2):109-122.
doi: 10.1007/s43465-019-00023-3. eCollection 2020 Apr.

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics

Murali Poduval  1 , Avik Ghose  2 , Sanjeev Manchanda  3 , Vaibhav Bagaria  4 , Aniruddha Sinha  2
Affiliations
Review

Artificial Intelligence and Machine Learning: A New Disruptive Force in Orthopaedics

Murali Poduval et al. Indian J Orthop. .

Abstract

Orthopaedics as a surgical discipline requires a combination of good clinical acumen, good surgical skill, a reasonable physical strength and most of all, good understanding of technology. The last few decades have seen rapid adoption of new technologies into orthopaedic practice, power tools, new implants, CAD-CAM design, 3-D printing, additive manufacturing just to name a few. The new disruption in orthopaedics in the current time and era is undoubtedly the advent of artificial intelligence and robotics. As these technologies take root and innovative applications continue to be incorporated into the main-stream orthopedics, as we know it today, it is imperative to look at and understand the basics of artificial intelligence and what work is being done in the field today. This article takes the form of a loosely structured narrative review and will introduce the reader to key concepts in the field of artificial intelligence as well as some of the directions in application of the same in orthopaedics. Some of the recent work has been summarised and we present our viewpoint at the conclusion as to why we must consider artificial intelligence as a disrupting positive influence on orthopaedic surgery.

Keywords: Artificial intelligence; Machine learning; Orthopaedic surgery.

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Conflict of interest statement

Conflict of interestThe authors declare no conflict of interests in relation to the content published here which is entirely educative and informative.

Figures

Fig. 1
Fig. 1
Evolution of Artificial Intelligence
Fig. 2
Fig. 2
Supervised learning: training prediction and feedback processing
Fig. 3
Fig. 3
Unsupervised learning process
Fig. 4
Fig. 4
A typical machine learning workflow
Fig. 5
Fig. 5
The machine learning pipeline
Fig. 6
Fig. 6
Some applications of Machine Learning in healthcare
See this image and copyright information in PMC

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