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

Aims. Prevalence of artificial intelligence (AI) algorithms within the Trauma & Orthopaedics (T&O) literature has greatly increased over the last ten years. One increasingly explored aspect of AI is the automated interpretation of free-text data often prevalent in electronic medical records (known as natural language processing (NLP)). We set out to review the current evidence for applications of NLP methodology in T&O, including assessment of study design and reporting. Methods. MEDLINE, Allied and Complementary Medicine (AMED), Excerpta Medica Database (EMBASE), and Cochrane Central Register of Controlled Trials (CENTRAL) were screened for studies pertaining to NLP in T&O from database inception to 31 December 2023. An additional grey literature search was performed. NLP quality assessment followed the criteria outlined by Farrow et al in 2021 with two independent reviewers (classification as absent, incomplete, or complete). Reporting was performed according to the Synthesis-Without Meta-Analysis (SWiM) guidelines. The review protocol was registered on the Prospective Register of Systematic Reviews (PROSPERO; registration no. CRD42022291714). Results. The final review included 31 articles (published between 2012 and 2021). The most common subspeciality areas included trauma, arthroplasty, and spine; 13% (4/31) related to online reviews/social media, 42% (13/31) to clinical notes/operation notes, 42% (13/31) to radiology reports, and 3% (1/31) to systematic review. According to the reporting criteria, 16% (5/31) were considered good quality, 74% (23/31) average quality, and 6% (2/31) poor quality. The most commonly absent reporting criteria were evaluation of missing data (26/31), sample size calculation (31/31), and external validation of the study results (29/31 papers). Code and data availability were also poorly documented in most studies. Conclusion. Application of NLP is becoming increasingly common in T&O; however, published article quality is mixed, with few high-quality studies. There are key consistent deficiencies in published work relating to NLP which ultimately influence the potential for clinical application. Open science is an important part of research transparency that should be encouraged in NLP algorithm development and reporting. Cite this article: Bone Jt Open 2025;6(3):264–274

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Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis.

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Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

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Autologous bone graft (ABG) is considered the ‘gold standard’ among graft materials for bone regeneration. However, complications including limited availability, donor site morbidity, and deterioration of regenerative capacity over time have been reported. P-15 is a synthetic peptide that mimics the cell binding domain of Type-I collagen. This peptide stimulates new bone formation by enhancing osteogenic cell attachment, proliferation, and differentiation. The objective of this study was to conduct a systematic literature review to determine the clinical efficacy and safety of P-15 peptide in bone regeneration throughout the skeletal system.