Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions

Abstract

Purpose of ReviewWith the unprecedented advancement of data aggregation and deep learning algorithms, artificial intelligence (AI) and machine learning (ML) are poised to transform the practice of medicine. The field of orthopedics, in particular, is uniquely suited to harness the power of big data, and in doing so provide critical insight into elevating the many facets of care provided by orthopedic surgeons. The purpose of this review is to critically evaluate the recent and novel literature regarding ML in the field of orthopedics and to address its potential impact on the future of musculoskeletal care. Recent Findings Recent literature demonstrates that the incorporation of ML into orthopedics has the potential to elevate patient care through alternative patient-specific payment models, rapidly analyze imaging modalities, and remotely monitor patients. Summary Just as the business of medicine was once considered outside the domain of the orthopedic surgeon, we report evidence that demonstrates these emerging applications of AI warrant ownership, leverage, and application by the orthopedic surgeon to better serve their patients and deliver optimal, value-based care.

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“…In recent years, the use of convolutional neural networks (CNN), the most popular deep learning models, has been recognized as a useful reasoning option for predicting the complex outcomes of patients . A CNN consists mainly of a convolutional layer, pooling layer and fully‐connected layer, which are responsible for feature extraction, reduction of the dimensionality of the features and feature classification, respectively .…”

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“…Broadly, AI is the science and engineering of creating intelligent machines that can achieve tasks that otherwise require human input. 5 Machine learning (ML) is a subset of AI that uses computational algorithms to analyze large data sets to classify and predict without explicit instructions. 5 , 6 In its most rudimentary form, ML models are given inputs and outputs of “training sets” using real-world data to determine relationships using pattern recognition.…”

“… 5 Machine learning (ML) is a subset of AI that uses computational algorithms to analyze large data sets to classify and predict without explicit instructions. 5 , 6 In its most rudimentary form, ML models are given inputs and outputs of “training sets” using real-world data to determine relationships using pattern recognition. 6 As such, the model is dependent on the accuracy and biases of the given data set.…”

“…Thus, algorithms possess the capacity to “reflect” by continually assessing and improving the quality of its analyses, with the potential to continue incremental learning after addition of new data so as to not “reinvent the wheel,” thereby permitting global data sharing and interconnectivity. 5 , 6 , 7 Understanding appropriate use for AI models is required prior for application to clinical scenarios; further discussion of the various models is outside the scope of this review.…”