The Role of AI in Musculoskeletal Teaching

Abstract

Background: Musculoskeletal (MSK) medicine has undergone a rapid transformation in recent years, with artificial intelligence (AI) emerging as a key driver of innovation. Existing literature has predominantly explored AI in diagnostic imaging, predictive analytics, and Natural Language Processing for clinical decision support. While several studies have touched upon AI’s potential in medical education, there remains limited evidence specifically evaluating its role in MSK teaching, the pedagogical outcomes, and integration into established curricula. This gap is critical given the changing dynamics of healthcare delivery, the need for scalable training solutions, and the increasing emphasis on personalised, technology-driven learning. The current work addresses this shortfall by synthesising recent evidence and examining how AI-based tools can enhance MSK education. Methods: A comprehensive literature search (2018–2025) was conducted across PubMed, Scopus, and Web of Science using predefined keywords relating to AI and MSK teaching. Eligible studies were peer-reviewed articles, case studies, and trials in English that met specific inclusion criteria. From the 342 screened articles, 43 were selected for detailed analysis. Data extraction focused on AI applications in MSK training, reported benefits, limitations, and integration strategies. Non-peer-reviewed and opinion-based sources were excluded. Inclusion criteria: Peer-reviewed articles, case studies, and trials published in English between January 2018 and 2025, focusing on AI applications in MSK education among healthcare professionals in training. Both qualitative and quantitative studies were included. Exclusion criteria: Non-peer-reviewed articles, editorials, opinion pieces without empirical evidence, studies on non-human/animal subjects, and studies unrelated to AI or MSK systems. Results: The literature reveals that AI has enhanced MSK training through immersive technologies such as 3D modelling, augmented reality (AR), and virtual reality (VR). These innovations reduce reliance on traditional cadaveric and apprenticeship-based models, offering cost-effective, scalable, and interactive learning. AI-driven platforms facilitate personalised case-based learning, improve diagnostic accuracy, and support tailored intervention planning. However, significant challenges remain—most notably, limited longitudinal data on educational outcomes, barriers to large-scale adoption (cost, infrastructure), data privacy concerns, and the absence of standardised frameworks for integrating AI into formal MSK curricula. Conclusion: While AI shows considerable promise in transforming MSK teaching, the field lacks robust, outcome-focused research to guide evidence-based adoption. Addressing these gaps through targeted studies, standardisation of teaching protocols, and blended integration with conventional training will be essential to realising AI’s full potential in enhancing both learner competence and patient care outcomes.

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