Machine Learning

Machine learning (ML), a type of artificial intelligence (AI), has been gaining popularity in every field of medicine. It can be identified as the ability of a machine to identify relationships among large amounts of available data without explicit criteria, emulating a human-like type of learning. Orthopedic surgery is a field that substantially relies on technological and industrial developments and has significant potential to lead advancements in the world of AI. ML has many applications in orthopedic surgery. In terms of diagnosis, attempts to predict pathology from X-ray, computed tomography, and magnetic resonance images have been in progress. For the surgical aid, studies utilized ML for implant recognition, preoperative templating, implant positioning, navigation, and risk assessment. Studies have shown that ML provides favorable outcomes. With the advancements in AI technologies, ML will surely become more and more functional in orthopedic surgery. However, it is not without drawbacks. It requires a large amount of data to function, which is not always readily available. Also, there are many ethical concerns for its use. This chapter focuses on the definition of ML, its applications, limitations, and what the future holds for ML in orthopedic surgery.