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:
The aim of this study was to determine how the short- and medium-
to long-term outcome measures after total disc replacement (TDR)
compare with those of anterior cervical discectomy and fusion (ACDF),
using a systematic review and meta-analysis. Databases including Medline, Embase, and Scopus were searched.
Inclusion criteria involved prospective randomized control trials
(RCTs) reporting the surgical treatment of patients with symptomatic
degenerative cervical disc disease. Two independent investigators
extracted the data. The strength of evidence was assessed using
the Grading of Recommendations, Assessment, Development and Evaluation
(GRADE) criteria. The primary outcome measures were overall and
neurological success, and these were included in the meta-analysis. Standardized
patient-reported outcomes, including the incidence of further surgery
and adjacent segment disease, were summarized and discussed.Aims
Patients and Methods
