Small language models: The big play for agentic artificial intelligence in orthopaedics
Övrig text i vetenskaplig tidskrift, 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.
small language models (SLMs)
artificial intelligence
agentic AI
orthopaedics
Författare
Felix C. Oettl
Universität Zürich
James Pruneski
Tripler Regional Med Center
Bálint Zsidai
Sahlgrenska Sports Medicine Center
Göteborgs universitet
Skånes universitetssjukhus (SUS)
Yinan Yu
Chalmers, Data- och informationsteknik, Funktionell programmering
Michael T. Hirschmann
University Department of Orthopaedic Surgery and Traumatology
Universität Basel
Kristian Samuelsson
Sahlgrenska universitetssjukhuset
Göteborgs universitet
Sahlgrenska Sports Medicine Center
Knee Surgery, Sports Traumatology, Arthroscopy
0942-2056 (ISSN) 1433-7347 (eISSN)
Vol. In PressÄmneskategorier (SSIF 2025)
Människa-datorinteraktion (interaktionsdesign)
Ortopedi
DOI
10.1002/ksa.70126
PubMed
41144760