Evaluating the Impact of AI in Orthopedics: A Quantitative Analysis of Advancements and Challenges

Abstract

Abstract This study systematically evaluates the development of artificial intelligence applications in orthopedics using bibliometric analysis methods. Data obtained from the Web of Science database, analyzing 1833 documents published between 1988 and 2024, reveal an annual growth rate of 19.45%, increased interdisciplinary collaboration, and a high level of international interaction. Co-occurrence analysis identifies key themes, including diagnostic classification methods utilizing image processing for spine and low back pain, AI-based modeling in diagnosis, risk assessment and management of orthopedic diseases, outcome evaluation, risk and quality assessments in orthopedic surgery and joint prostheses, as well as orthopedic trauma and reconstruction methods. Co-citation analysis highlights themes such as the integration of machine learning models into clinical applications, the use of artificial intelligence in spine surgery, current knowledge and practical application guidelines, spine metastases and clinical decision support systems, deep learning techniques in imaging and diagnostics, patient-based payment modeling and health economics, and AI-supported patient communication and clinical information systems. Referenced Publication Years Spectroscopy (RPYS) analysis indicates that foundational studies and key breakthroughs were concentrated in the years 2001, 2010, 2015, and 2019. Recent publications support the application of artificial intelligence in areas such as tibiofemoral cartilage strain analysis, mechanical alignment, automatic segmentation, clinical prediction models, and pediatric orthopedics through deep learning and machine learning techniques.

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