Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

Objectives

Whilst gait speed is variable between healthy and injured adults, the extent to which speed alone alters the 3D in vivo knee kinematics has not been fully described. The purpose of this prospective study was to understand better the spatiotemporal and 3D knee kinematic changes induced by slow compared with normal self-selected walking speeds within young healthy adults.

Objectives

Patient-reported outcome measures (PROMs) are often used to evaluate the outcome of treatment in patients with distal radial fractures. Which PROM to select is often based on assessment of measurement properties, such as validity and reliability. Measurement properties are assessed in clinimetric studies, and results are often reviewed without considering the methodological quality of these studies. Our aim was to systematically review the methodological quality of clinimetric studies that evaluated measurement properties of PROMs used in patients with distal radial fractures, and to make recommendations for the selection of PROMs based on the level of evidence of each individual measurement property.