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.

Many aspects of total knee arthroplasty have changed since its inception. Modern prosthetic design, better fixation techniques, improved polyethylene wear characteristics and rehabilitation, have all contributed to a large change in revision rates. Arthroplasty patients now expect longevity of their prostheses and demand functional improvement to match. This has led to a re-examination of the long-held belief that mechanical alignment is instrumental to a successful outcome and a focus on restoring healthy joint kinematics. A combination of kinematic restoration and uncemented, adaptable fixation may hold the key to future advances. Cite this article: Bone Joint J 2015;97-B(10 Suppl A):16–19