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

The importance of registries has been brought into focus by recent UK national reports focusing on implant (Cumberlege) and surgeon (Paterson) performance. National arthroplasty registries provide real-time, real-world information about implant, hospital, and surgeon performance and allow case identification in the event of product recall or adverse surgical outcomes. They are a valuable resource for research and service improvement given the volume of data recorded and the longitunidal nature of data collection. This review discusses the current value of registry data as it relates to both clinical practice and research.

Cite this article: Bone Joint J 2023;105-B(4):356–360.

There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines.

Cite this article: Bone Joint J 2021;103-B(12):1754–1758.

Public disclosure of outcome-orientated ranking of hospitals is becoming increasingly popular and is routinely used by Swedish health-care authorities. Whereas uncertainty about an outcome is usually presented with 95% confidence intervals, ranking’s based on the same outcome are typically presented without any concern for bias or statistical precision. In order to study the effect of incomplete registration of re-operation on hospital ranking we performed a simulation study using published data on the two-year risk of re-operation after total hip replacement. This showed that whereas minor registration incompleteness has little effect on the observed risk of revision, it can lead to major errors in the ranking of hospitals. We doubt whether a level of data entry sufficient to generate a correct ranking can be achieved, and recommend that when ranking hospitals, the uncertainties about data quality and random events should be clearly described as an integral part of the results

With the established success of the National Joint Registry and the emergence of a range of new national initiatives for the capture of electronic data in the National Health Service, orthopaedic surgery in the United Kingdom has found itself thrust to the forefront of an information revolution. In this review we consider the benefits and threats that this revolution poses, and how orthopaedic surgeons should marshal their resources to ensure that this is a force for good.

This article considers some of the problems of the interpretation of information from other national arthroplasty registers when setting up a new register. In order for the most useful information to be available from registers much international co-operation is required between all those responsible for the design of registers as well as those who gather, assess and publish the data.