Banskota Bibek, Bhusal Rajan, Yadav Prkash Kumar, Banskota Ashok Kumar
Hospital and Rehabilitation Centre for the Disabled Children, Banepa. Kavre, Nepal.
B and B Hospital, Lalitpur, Nepal.
BMC Med Educ. 2025 Nov 13;25(1):1594. doi: 10.1186/s12909-025-08162-y.
Artificial intelligence (AI) increasingly transforms orthopedic education and research, offering novel surgical training and scientific advancement approaches. However, these AI applications' scope, impact, and limitations require comprehensive evaluation. Aim to systematically review AI technologies' practical applications, benefits, limitations, and future directions in orthopedic education and research.
This systematic review was registered with PROSPERO (CRD420251064596). A comprehensive search of PubMed, Embase, and Scopus was conducted. Studies reporting AI technologies-such as machine learning, virtual reality (VR) simulation, generative AI, and adaptive learning systems-were included in orthopedic training or research. Due to heterogeneity in study designs and outcome measures, data were extracted and synthesized narratively.
AI technologies consistently improve training efficiency, personalized learning, and objective skill assessments. VR simulation and machine learning-based feedback tools enhanced technical proficiency and significantly reduced learning curves. Adaptive learning platforms enabled tailored educational pathways. However, generative AI applications remain nascent, with notable concerns regarding content accuracy and bias. Across studies, key limitations included data bias, over-reliance on automated systems, high implementation costs, and a lack of longitudinal and real-world validation. Most studies were limited to simulation-based environments with insufficient evidence on clinical skill transfer. Ethical, curricular, and regulatory considerations remain underdeveloped.
AI holds considerable potential to advance orthopedic education and research by enabling more efficient, equitable, and personalized training. However, its integration must be guided by rigorous validation, ethical standards, and stakeholder collaboration. Hybrid human-AI training models and standardized evaluation metrics are essential to realizing AI's full potential in orthopedics.
