The Bone & Joint Journal has published several consensus statements in recent years, many of which have positively influenced clinical practice and policy.^1-13 However, even the most valued consensus statements have limitations, and all ultimately represent Level V evidence. Consensus studies add greatest value where higher-order evidence to aid decision making is ambiguous or lacking. In all settings, care must be taken to critically appraise standards of methodology, with particular attention to potential biases that may influence the conclusions which are drawn.

Cite this article: Bone Joint J 2023;105-B(4):343–346.

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.