Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
In this review, we discuss the evidence for patients returning to sport after hip arthroplasty. This includes the choices regarding level of sporting activity and revision or complications, the type of implant, fixation and techniques of implantation, and how these choices relate to health economics. It is apparent that despite its success over six decades, hip arthroplasty has now evolved to accommodate and support ever-increasing patient demands and may therefore face new challenges. Cite this article:
