Publication Trends and Citation Patterns of Artificial Intelligence and Large Language Model Research in Orthopedics and Traumatology: A Türkiye-based Bibliometric Analysis

Introduction: Artificial intelligence (AI), particularly large language models (LLMs) and generative systems such as ChatGPT, has rapidly transformed medical research and clinical practice. Although global publication trends have been widely examined, country-specific bibliometric analyses in orthopedics and traumatology remain limited. This study aimed to perform a comprehensive bibliometric evaluation of Türkiye-based AI and LLM research in orthopedics and traumatology and compare the findings with global trends.
Methods: A cross-sectional bibliometric analysis was conducted in January 2026 using PubMed, TR Dizin, Scopus, and Web of Science databases. Original research articles from Türkiye-affiliated institutions involving AI, machine learning, deep learning, or large language model applications in orthopedics and musculoskeletal research were included. Journal Impact Factor (2024 JCR), citation counts, and SCImago Journal Rank (SJR) quartile classifications were recorded. Descriptive statistical analyses were performed.
Results: A total of 63 studies were included. Publication volume increased markedly after 2023. The mean Journal Impact Factor was 2.34±1.15 (median: 2.20; range: 0.8–5.4). Of the publications, 38.1% were in Q1 and 46.0% in Q2 journals, with none in Q4. The total citation count was 578, with a mean of 9.17±14.20 (median: 5; range: 0–82), reflecting a right-skewed distribution. Patient education studies were numerically predominant (n=34, 53.9%), while clinical application studies demonstrated the highest mean citation count (10.5±18.1).
Discussion and Conclusion: AI-based research in orthopedics and traumatology in Türkiye has expanded rapidly since 2023, predominantly in Q1–Q2 journals. Citation patterns show considerable heterogeneity, consistent with an early developmental phase. These findings provide an objective bibliometric profile and may inform country-specific scientific strategies in AI-driven orthopedic research.