Total hip and knee arthroplasty (THA, TKA) are largely successful procedures; however, both have variable outcomes, resulting in some patients being dissatisfied with the outcome. Surgeons are turning to technologies such as robotic-assisted surgery in an attempt to improve outcomes. Robust studies are needed to find out if these innovations are really benefitting patients. The Robotic Arthroplasty Clinical and Cost Effectiveness Randomised Controlled Trials (RACER) trials are multicentre, patient-blinded randomized controlled trials. The patients have primary osteoarthritis of the hip or knee. The operation is Mako-assisted THA or TKA and the control groups have operations using conventional instruments. The primary clinical outcome is the Forgotten Joint Score at 12 months, and there is a built-in analysis of cost-effectiveness. Secondary outcomes include early pain, the alignment of the components, and medium- to long-term outcomes. This annotation outlines the need to assess these technologies and discusses the design and challenges when conducting such trials, including surgical workflows, isolating the effect of the operation, blinding, and assessing the learning curve. Finally, the future of robotic surgery is discussed, including the need to contemporaneously introduce and evaluate such technologies.

Cite this article: Bone Joint J 2024;106-B(2):114–120.

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.

Unicompartmental knee arthroplasty (UKA) has numerous advantages over total knee arthroplasty (TKA) and one disadvantage, the higher revision rate. The best way to minimize the revision rate is for surgeons to use UKA for at least 20% of their knee arthroplasties. To achieve this, they need to learn and apply the appropriate indications and techniques. This would decrease the revision rate and increase the number of UKAs which were implanted, which would save money and patients would benefit from improved outcomes over their lifetime. Cite this article: Bone Joint J 2018;100-B:432–5

We recently published a paper comparing the incidence of adverse outcomes after unicompartmental and total knee arthroplasty (UKA and TKA). The conclusion of this study, which was in favour of UKA, was dismissed as “biased” in a review in Bone & Joint 360. Although this study is one of the least biased comparisons of UKA and TKA, this episode highlights the biases that exist both for and against UKA. In this review, we explore the different types of bias, particularly selection, reporting and measurement. We conclude that comparisons between UKA and TKA are open to bias. These biases can be so marked, particularly in comparisons based just on national registry data, that the conclusions can be misleading. For a fair comparison, data from randomised studies or well-matched, prospective observational cohort studies, which include registry data, are required, and multiple outcome measures should be used. The data of this type that already exist suggest that if UKA is used appropriately, compared with TKA, its advantages outweigh its disadvantages. Cite this article: Bone Joint J 2017;99-B:12–15