The use of robots in orthopaedic surgery is an emerging field that is gaining momentum. It has the potential for significant improvements in surgical planning, accuracy of component implantation and patient safety. Advocates of robot-assisted systems describe better patient outcomes through improved pre-operative planning and enhanced execution of surgery. However, costs, limited availability, a lack of evidence regarding the efficiency and safety of such systems and an absence of long-term high-impact studies have restricted the widespread implementation of these systems. We have reviewed the literature on the efficacy, safety and current understanding of the use of robotics in orthopaedics. Cite this article: Bone Joint J 2015; 97-B:292–9

Aims

The principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.

Aims

The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments.