Small language models: The big play for agentic artificial intelligence in orthopaedics
Other text in scientific journal, 2025
While the integration of artificial intelligence in orthopedics is accelerating, the focus has largely been on powerful but resource-intensive Large Language Models (LLMs). This editorial argues for a strategic shift towards Small Language Models (SLMs) for many specialized clinical applications. SLMs offer a more efficient, cost-effective, and adaptable solution for the narrowly-scoped tasks common in orthopedics. We discuss their potential in surgical assistance, personalized patient management, and administrative automation, positing that the future of practical AI in our field lies in a diverse ecosystem of specialized SLMs. However, we also underscore that rigorous validation and the development of robust evaluation benchmarks are critical to ensure their safe and trustworthy integration into clinical practice.
artificial intelligence
orthopaedics
small language models (SLMs)
agentic AI
Author
Felix C. Oettl
University of Zürich
James Pruneski
Tripler Regional Med Center
Bálint Zsidai
Skåne University Hospital
Sahlgrenska University Hospital
University of Gothenburg
Yinan Yu
Chalmers, Computer Science and Engineering (Chalmers), Functional Programming
Michael T. Hirschmann
Canton Hospital Basel-Land
University of Basel
Kristian Samuelsson
Sahlgrenska University Hospital
University of Gothenburg
Knee Surgery, Sports Traumatology, Arthroscopy
0942-2056 (ISSN) 1433-7347 (eISSN)
Vol. In PressSubject Categories (SSIF 2025)
Human Computer Interaction
Orthopaedics
DOI
10.1002/ksa.70126
PubMed
41144760