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.
Cell therapies hold significant promise for the treatment of injured or diseased musculoskeletal tissues. However, despite advances in research, there is growing concern about the increasing number of clinical centres around the world that are making unwarranted claims or are performing risky biological procedures. Such providers have been known to recommend, prescribe, or deliver so called ‘stem cell’ preparations without sufficient data to support their true content and efficacy. In this annotation, we outline the current environment of stem cell-based treatments and the strategies of marketing directly to consumers. We also outline the difficulties in the regulation of these clinics and make recommendations for best practice and the identification and reporting of illegitimate providers. Cite this article:
