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Large Language Models in Preclinical Spine Research: A Scoping Review and Expert Perspective on Evidence-Aware

Siegmund Lang1, Stefan Motov2, Jonas Krueckel1

  • 1Department of Trauma Surgery University Hospital Regensburg Regensburg Germany.

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|July 9, 2026
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Summary

Large language models (LLMs) show potential in preclinical spine research for structuring data. However, current applications are rare, highlighting a need for validation and governance for responsible use.

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Area of Science:

  • Biomedical research
  • Spine science
  • Artificial intelligence

Background:

  • Preclinical spine research faces challenges with inconsistent reporting and reproducibility.
  • Artificial intelligence (AI) and large language models (LLMs) offer potential solutions for data interpretation and extraction.
  • The role of AI/LLMs in experimental spine science requires thorough investigation.

Purpose of the Study:

  • To map current AI/LLM applications in spine research.
  • To quantify the evidence gap in preclinical spine research concerning AI/LLMs.
  • To identify opportunities for responsible integration of AI/LLMs.

Main Methods:

  • A systematic literature search was conducted across PubMed, Embase, and Web of Science (Jan 2020-Jan 2026).
  • Studies evaluated LLM, AI, chatbot, or advanced natural language processing in spine-related contexts.
  • A focused analysis examined preclinical and translational use cases, supplemented by expert synthesis.

Main Results:

  • 166 unique studies met inclusion criteria, with increasing publication activity over time.
  • The majority of applications focused on conversational AI for patient assessment and education.
  • Preclinical applications of AI/LLMs were rare (1.8%), primarily using classical machine learning, not generative LLMs.

Conclusions:

  • Large language models (LLMs) can serve as valuable human-supervised tools for organizing fragmented experimental data in preclinical spine research.
  • Spine-specific validation and strong governance frameworks are crucial for the ethical and effective translational application of these technologies.