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Small language models: The big play for agentic artificial intelligence in orthopaedics

Year: 2025

Type: editorial

Abstract: 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.

Source: Zurich Open Repository and Archive (University of Zurich)

DOI: 10.5167/uzh-281573

Authors: Oettl, Felix C, Pruneski, James A, Zsidai, Balint, Yu, Yinan, Hirschmann, Michael T, Samuelsson, Kristian

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7124731153

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