Objectives: The medical community is in agreement that artificial intelligence (AI) will have a radical impact on patient care in the near future. The purpose of this study is to assess the awareness of AI technologies among health professionals and to investigate their perceptions toward AI applications in medicine. Design: A web-based Google Forms survey was distributed via the Royal Free London NHS Foundation Trust e-newsletter. Setting: Only staff working at the NHS Foundation Trust received an invitation to complete the online questionnaire. Participants: 98 healthcare professionals out of 7,538 (response rate 1.3%; CI 95%; margin of error 9.64%) completed the survey, including medical doctors, nurses, therapists, managers, and others. Primary outcome: To investigate the prior knowledge of health professionals on the subject of AI as well as their attitudes and worries about its current and future applications. Results: 64% of respondents reported never coming across applications of AI in their work and 87% did not know the difference between machine learning and deep learning, although 50% knew at least one of the two terms. Furthermore, only 5% stated using speech recognition or transcription applications on a daily basis, while 63% never utilize them. 80% of participants believed there may be serious privacy issues associated with the use of AI and 40% considered AI to be potentially even more dangerous than nuclear weapons. However, 79% also believed AI could be useful or extremely useful in their field of work and only 10% were worried AI will replace them at their job. Conclusions: Despite agreeing on the usefulness of AI in the medical field, most health professionals lack a full understanding of the principles of AI and are worried about potential consequences of its widespread use in clinical practice. The cooperation of healthcare workers is crucial for the integration of AI into clinical practice and without it the NHS may miss out on an exceptionally rewarding opportunity. This highlights the need for better education and clear regulatory frameworks.

Objectives To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period. Methods We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative—the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation—we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary. Key predictors of progression were identified through advanced interpretability techniques, and subgroup analyses were conducted by age, sex and ethnicity with a focus on early-stage disease. Results Although the most reliable models incorporated all available features, simpler models including only clinical variables achieved robust external validation performance, with area under the precision-recall curve (AUC-PRC) 0.727 (95% CI: 0.726 to 0.728) for multiclass predictions; and AUC-PRC 0.764 (95% CI: 0.762 to 0.766) for binary predictions. Multiclass models performed best in patients with early-stage OA (AUC-PRC 0.724–0.806) whereas binary models were more reliable in patients younger than 60 (AUC-PRC 0.617–0.693). Patient-reported outcomes and MRI features emerged as key predictors of progression, though subgroup differences were noted. Finally, we developed web-based applications to visualise our personalised predictions. Conclusions Our novel tool’s transparency and reliability in predicting rapid knee OA progression distinguish it from conventional ‘black-box’ methods and are more likely to facilitate its acceptance by clinicians and patients, enabling effective implementation in clinical practice.

Aims: Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.Methods: A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.Results: Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations.Conclusion: Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice.Cite this article: Bone Joint J 2024;106-B(11):1216–1222.

Background: This literature review examines the prevalence and severity of depression and anxiety symptoms in adolescents following ACL injury, with a focus on gender-disaggregated data. Methods: An electronic search was conducted across databases, including Ovid MEDLINE (R), PUBMED, and the Cochrane Library, covering articles from 1964 to 13 February 2024. Primary search terms were “ACL,” “depression,” “anxiety,” and “adolescent,” expanded using medical subject headings (MeSH). Manual searches of the references supplemented the database search. Inclusion criteria included studies on depression or anxiety post-ACL injury in adolescents. The exclusion criteria were studies without adolescent participants or those focusing exclusively on one sex. Results: The search identified 62 studies, of which 5 were ultimately selected for comprehensive analysis. Four studies reported on depression using various scales, and three measured anxiety symptoms. No studies provided gender-disaggregated data. One study found no significant difference in depression scores between adolescent and adult patients. Another study noted that socioeconomic disadvantage correlated with increased depressive and anxiety symptoms post-surgery. An additional study highlighted a significant increase in depression scores from pre- to post-injury among ACL-injured participants compared to uninjured controls. Conclusions: Our understanding of the psychological consequences of ACL rupture on female adolescent patients is incomplete. Given the high incidence of ACL injuries in this patient group and their potential psychological vulnerability, improving the evidence base in this area could address a previously neglected aspect of care, with positive impacts on returning to sport and quality of life. Future studies of high methodological quality are needed to address this gap in the literature.