Abstract
BACKGROUND: Vertebral compression fractures (VCF) are a common cause of pain and disability, particularly in the aging population. Although artificial intelligence (AI) has shown promise across various medical domains, its application in VCF diagnosis and treatment remains fragmented. A comprehensive understanding of the research trends and key contributors to this field is lacking. OBJECTIVE: This study aimed to map the knowledge landscape of AI applications in VCF through bibliometric analysis, identifying temporal patterns, intellectual hotspots, and influential contributors to guide future research. METHODS: A total of 462 English-language articles published between 2004 and 2023 were retrieved from the Web of Science Core Collection. CiteSpace 6.2.R6 was used to perform the co-authorship, keyword co-occurrence, citation burst, and clustering analyses. Parameters such as time-slicing, g-index (k = 50), and pathfinder network scaling were applied. The key metrics included publication trends, keyword bursts, and centrality scores. Statistical trends were visualized to identify the developmental inflection points and thematic shifts. RESULTS: The number of publications increased modestly until 2018, followed by a notable surge in 2019, which marked the rapid integration of AI-intensive learning into VCF research. Keyword analysis revealed a thematic evolution from traditional procedures (e.g., vertebroplasty) to AI-driven diagnostics and robotic-assisted interventions. "Deep learning" exhibited the strongest citation burst since 2019. Influential authors, such as Bizhan Aarabi, and institutions in the United States and China were prominent, with SPINE identified as the most frequently cited journal. CONCLUSION: AI technologies, especially deep learning and robot-assisted surgery, have become transformative tools in the VCF domain, enhancing diagnostic accuracy and treatment precision. This bibliometric analysis reveals a shift toward technology-driven research paradigms and highlights the critical actors and trends shaping the field. Ongoing interdisciplinary collaboration and clinical validation are essential to fully realize AI's potential of AI in orthopedic care and improve patient outcomes.