Large language models in orthopedics: An exploratory research trend analysis and machine learning classification
| dc.coverage | DOI: 10.1016/j.jor.2024.12.039 | |
| dc.creator | Velasquez Garcia, Ausberto | |
| dc.creator | Minami, Masataka | |
| dc.creator | Mejia-Rodríguez, Manuel | |
| dc.creator | Ortíz-Morales, Jorge Rolando | |
| dc.creator | Radice, Fernando | |
| dc.date | 2025 | |
| dc.date.accessioned | 2026-01-05T21:09:30Z | |
| dc.date.available | 2026-01-05T21:09:30Z | |
| dc.description | <p>Background: Large Language Models (LLMs) are set to transform orthopedic practice with promising applications and a growing body of research. This exploratory study analyzed research trends in orthopedic LLMs and validated a machine learning classifier for categorizing publications into predefined domains. We hypothesized that LLM-related research would exhibit distinct thematic trends, and that a machine learning classifier would be able to accurately categorize research domains. Methods: A bibliometric analysis of 140 Scopus-indexed publications (2019–2024) was performed using keyword co-occurrence and thematic clustering. Articles were categorized into five areas: Patient Education, Research and Ethics, Surgeon Education, Clinical Support, and Diagnostics and Radiology Interpretation. Machine learning classifiers were trained on TF-IDF vectorized text and evaluated using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Exploratory projections using linear regression assessed the volume and growth trends within the five research areas. Results: The exploratory analysis revealed a substantial increase in LLM publications increased significantly from 28 in 2023 to 108 articles in 2024. The support vector machine (SVM) model outperformed others, achieving 82 % accuracy (AUC-ROC: 0.97), with high precision for categorizing research in Clinical Assistance Tools and strong recall for Diagnosis and Radiology Interpretation. Subgroup analysis showed that Patient Education achieved balanced performance (precision: 88 %, recall: 78 %, F1-score: 82 %), but overlapping terminology caused misclassifications between research and education domains. Temporal analysis predicted continued growth in these research domains, with Patient Education (+26 %) and Research and Ethics (+57 %) leading the way through 2027. Conclusion: LLMs are exploring advancements in patient engagement, surgeon training, and orthopedic research, but challenges in reliability and ethics require careful implementation. Future work should focus on real-world validation, specialty-specific applications, and integrating multimodal AI systems. The SVM classifier demonstrated robust capabilities, providing a valuable tool for navigating the growing body of literature.</p> | eng |
| dc.identifier | https://investigadores.uandes.cl/en/publications/643bcfdb-baa9-4c7d-8e04-1a825e83100b | |
| dc.identifier.uri | https://repositorio.uandes.cl/handle/uandes/63884 | |
| dc.language | eng | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | |
| dc.source | vol.66 (2025) p.110-118 | |
| dc.subject | Chat GPT | |
| dc.subject | Large language models (LLMs) | |
| dc.subject | Machine learning classification | |
| dc.subject | Orthopedic research | |
| dc.subject | Thematic clustering | |
| dc.title | Large language models in orthopedics: An exploratory research trend analysis and machine learning classification | eng |
| dc.type | Article | eng |
| dc.type | Artículo | spa |