Editorial Commentary : Shifting From Redundancy to Rigor in Orthopaedic Large Language Model Research
Open accessAbstract
Large language model (LLM) research in musculoskeletal medicine is growing rapidly, but much of the literature remains methodologically weak and highly repetitive. To address this, the orthopaedic LLM research community needs a shared benchmarking infrastructure/framework to evaluate models on clinically grounded tasks using fixed‐prompt templates and transparent scoring. Drawing on established LLM benchmarking practices, such a framework would enable reproducibility, discourage cherry‐picking, and promote meaningful innovation. Like surgical registries in orthopaedics, open LLM benchmarks can clarify performance, guide adoption, and ensure that progress is both measurable and clinically relevant.
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Prior benchmark studies can quickly become outdated as new model versions are released.
“Given the rapid evolution of LLMs, prior benchmark studies can quickly become outdated as new model versions are released (Yang et al. 2025).”
From Evidence on Demand: OpenEvidence versus Established LLMs on the Orthopaedic In-Training Examination (2026) · cites this paper as Yang et al. 2025
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Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., & Szolovits, P. (2021) · Applied Sciences · doi:10.3390/app11146421 · Cited by 718 · see citing works
The application of large language models in medicine: A scoping review
Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109713 · Cited by 414 · see citing works
Measuring Mathematical Problem Solving With the MATH Dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., & Steinhardt, J. (2021) · arXiv · doi:10.48550/arxiv.2103.03874 · Cited by 306 · see citing works
Measuring Massive Multitask Language Understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., & Steinhardt, J. (2020) · arXiv · doi:10.48550/arxiv.2009.03300 · Cited by 291 · see citing works
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
Chiang, W.-L., Zheng, L., Sheng, Y., Angelopoulos, A. N., Li, T., Li, D., Zhang, H., Zhu, B., Jordan, M., Gonzalez, J. E., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.04132 · Cited by 118
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Evidence on Demand: OpenEvidence versus Established LLMs on the Orthopaedic In-Training Examination
Khan, A. A., Lalvani, S. S., Pourarbab, S., Hyeamang, L. J., Lew, R. M., Hussain, Z. B., Nicholson, G. P., & Garrigues, G. E. (2026) · Journal of Orthopaedic Experience & Innovation · doi:10.60118/001c.162882
- Authors
- Yang, A. J., Woo, J. J., & Ramkumar, P. N.
- Affiliations
- Brown University (US)
- Venue
- Arthroscopy
- Publisher
- Wiley
- Published
- 2025-08-04
- Volume
- 41
- Issue
- 11
- Pages
- 4946-4949
- ISSN
- 0749-8063 (ISSN-L)
- 1526-3231
- OpenAlex
- W4413051367
- Cited by
- 4
- References
- 5
- Citations by year
- 2026: 4
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Arthroscopy The Journal of Arthroscopic and Related Surgery (publishedVersion, other-oa)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- large language models, orthopaedic research, LLM benchmarking, musculoskeletal medicine, reproducibility, cherry-picking, prompt templates
- MeSH terms
- Large Language Models, Humans, Language, Orthopedics, Benchmarking, Biomedical Research
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