The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
Preprint servers allow authors to publish full-text manuscripts or interim findings prior to undergoing peer review. Several preprint servers have extended their services to biological sciences, clinical research, and medicine. The purpose of this study was to systematically identify and analyze all articles related to Trauma & Orthopaedic (T&O) surgery published in five medical preprint servers, and to investigate the factors that influence the subsequent rate of publication in a peer-reviewed journal. All preprints covering T&O surgery were systematically searched in five medical preprint servers (medRxiv, OSF Preprints, Preprints.org, PeerJ, and Research Square) and subsequently identified after a minimum of 12 months by searching for the title, keywords, and corresponding author in Google Scholar, PubMed, Scopus, Embase, Cochrane, and the Web of Science. Subsequent publication of a work was defined as publication in a peer-reviewed indexed journal. The rate of publication and time to peer-reviewed publication were assessed. Differences in definitive publication rates of preprints according to geographical origin and level of evidence were analyzed.Aims
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Studies have addressed the issue of increasing prevalence of work-related musculoskeletal (MSK) pain among different occupations. However, contributing factors to MSK pain have not been fully investigated among orthopaedic surgeons. Thus, this study aimed to approximate the prevalence and predictors of MSK pain among Saudi orthopaedic surgeons working in Riyadh, Saudi Arabia. A cross-sectional study using an electronic survey was conducted in Riyadh. The questionnaire was distributed through email among orthopaedic surgeons in Riyadh hospitals. Standardized Nordic questionnaires for the analysis of musculoskeletal symptoms were used. Descriptive measures for categorical and numerical variables were presented. Student’s t-test and Pearson’s χ2 test were used. The level of statistical significance was set at p ≤ 0.05.Introduction
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Objectives. Despite the fact that research
