Key Takeaways & Executive Findings
- •• The number of publications on AI in orthopedic imaging has steadily increased from 2015 to 2025, with a surge after 2017. • The United States leads in citation impact and international collaboration, while China is a major contributor in terms of publication volume. • Research hotspots include bone age assessment, automated segmentation, and deep learning applications in fracture detection and osteoarthritis diagnosis. • Future directions emphasize intelligent segmentation, disease grading, and multimodal data fusion.
Abstract
BACKGROUND: In the process of applying artificial intelligence to orthopedic imaging, the technical system exhibits a clear hierarchical structure: machine learning is the primary pathway to achieving artificial intelligence, while convolutional neural networks, a branch of deep learning, have become the core model for image analysis. Clarifying this technical lineage helps to systematically review the research evolution and trends in this field through bibliometric methods. OBJECTIVE: To comprehensively analyze the research status and development trends of artificial intelligence in the field of orthopedic imaging based on bibliometric methods, providing ideas and methods for future research. METHODS: By searching the Web of Science Core Collection database, with keywords including artificial intelligence, deep learning, convolutional neural network, and orthopedic imaging, a total of 460 relevant English articles published between 2015 and 2025 were included. CiteSpace 6.4.R1, VOSviewer 1.6.20, and Bibliometrix software were used to conduct visual analysis from dimensions such as annual publication volume, country and institution distribution, author collaboration network, keyword co-occurrence, clustering, and burst word evolution. RESULTS AND CONCLUSION: (1) The number of publications in this field has steadily increased over the past 10 years. (2) China and the United States are the main publishing countries, with the United States showing outstanding performance in citation frequency and international collaboration influence; Sichuan University, the University of California, and Harvard University constitute a core collaborative institutional network. (3) Research hotspots mainly focus on bone age assessment, automated image segmentation, and the application of deep learning in fracture detection and osteoarthritis diagnosis. Related keywords such as bone age assessment, automated segmentation, and deep learning have continued to burst, indicating the evolutionary trajectory of research focus. (4) The research enthusiasm for artificial intelligence in orthopedic imaging continues to rise, with intelligent segmentation, disease grading, and multimodal data fusion being important future research directions. (5) This paper systematically reviews the field from a macro perspective, providing a reference for promoting the deep integration of artificial intelligence technology in orthopedic clinical practice; through bibliometric analysis, it constructs a knowledge map of the application of artificial intelligence in orthopedic imaging, systematically summarizes the research status and hotspots in this field, and aims to provide reference and guidance for future related research.
1. Introduction
According to the World Health Organization (WHO) and related epidemiological studies, musculoskeletal diseases have become one of the diseases with the highest global disability rate, affecting the quality of life and work ability of billions of people, and continue to grow with population aging and increased life expectancy [1-2]. Osteoarthritis alone affects hundreds of millions of adults worldwide, while osteoporotic fractures are important risk factors for disability and death in the elderly [3]. Imaging examination is a key means for diagnosing and treating orthopedic diseases. Through X-ray, CT, MRI and other technologies, doctors can accurately assess the location and extent of lesions and formulate personalized treatment plans. With the rapid development of artificial intelligence technology, its application in orthopedic imaging has become increasingly widespread, significantly improving diagnostic efficiency and accuracy. In 2024, China issued the 'Expert Consensus on Ethical Requirements for Medical Data Processing by Artificial Intelligence', which puts forward ethical and legal requirements for the collection, processing, storage, transmission, use, and sharing of medical data, aiming to protect patient rights and data security in AI-driven medical practice. In addition, the National Medical Products Administration (NMPA) also issued the 'Guiding Principles for Registration Review of Artificial Intelligence Medical Devices (Draft for Comments)', emphasizing algorithm transparency, risk management, and full lifecycle supervision. These guidelines and consensus provide an institutional basis for the safe and controllable application of AI in medical imaging.
As a cutting-edge interdisciplinary field, artificial intelligence's technical system covers multiple dimensions such as perceptual computing, cognitive intelligence, and decision support systems [4]. It enhances diagnostic efficiency and accuracy through precise recognition and intelligent analysis, providing more efficient and personalized solutions for the treatment of orthopedic diseases, especially in key areas such as three-dimensional reconstruction, parametric 3D printing implant design, automatic recognition and quantitative analysis of pathological features, and intelligent surgical path planning.
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Yue Yuhang, Xie Liangyu, Shi Liupeng, Yin Zuozhen, Cao Shengnan, Shi Bin, Sun Guodong (2026). Bibliometric analysis of application of artificial intelligence in orthopedic imaging diagnosis. Chinese Journal of Tissue Engineering Research. https://doi.org/10.12307/2026.21368
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Frequently Asked Questions
What is the main objective of this bibliometric analysis?
The main objective is to comprehensively analyze the research status and development trends of artificial intelligence in orthopedic imaging using bibliometric methods, providing ideas and methods for future research.
Which databases and tools were used in this study?
The study used the Web of Science Core Collection database and employed CiteSpace 6.4.R1, VOSviewer 1.6.20, and Bibliometrix software for visualization analysis.
What are the key research hotspots identified in this field?
Key research hotspots include bone age assessment, automated image segmentation, and the application of deep learning in fracture detection and osteoarthritis diagnosis.
Which countries and institutions are leading in this research area?
China and the United States are the main publishing countries, with the United States leading in citation impact and international collaboration. Key institutions include Sichuan University, the University of California, and Harvard University.
What are the future research directions suggested by this study?
Future research directions include intelligent segmentation, disease grading, and multimodal data fusion, as well as addressing technical and ethical challenges in clinical implementation.
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