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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.
Background:
Preclinical spine research is limited by heterogeneous experimental reporting, fragmented documentation, and barriers to reproducibility and translational alignment. Large language models (LLMs) and related artificial intelligence (AI) technologies may support semantic interpretation, structured data extraction, and reasoning over biomedical text, but their role in experimental spine science remains unclear. This focused scoping review and expert perspective mapped current AI/LLM applications in spine research, quantified the preclinical evidence gap, and identified responsible integration opportunities.
Methods:
A structured search of PubMed, Embase, and Web of Science was performed for studies published from January 2020 to January 2026 evaluating LLM, AI, chatbot, or advanced natural language processing applications in spine-related contexts. The search intentionally captured both LLM-specific and broader AI/chatbot applications to map the translational landscape. For this preclinical-focused analysis, the corpus was re-examined for experimental and translational use cases, supplemented by expert synthesis of methodologically relevant adjacent biomedical literature.
Results:
Of 792 records identified, 166 unique studies met inclusion criteria. Publication activity increased markedly over time. The literature was dominated by conversational assessment/patient-reported outcome measure applications (82/166; 49.4%), patient education/information quality studies (53/166; 31.9%), and other LLM/AI applications (19/166; 11.4%). Preclinical/basic science applications were rare (3/166; 1.8%) and used classical machine learning, deep learning, or broader AI frameworks rather than generative LLMs. The most credible near-term opportunities include schema-constrained data extraction, protocol completeness checking, ontology-aligned data structuring, and evidence-grounded workflow support under human supervision.
Conclusion:
In preclinical spine research, LLMs are best positioned as human-supervised workflow instruments for structuring fragmented experimental knowledge. Spine-specific validation and robust governance are essential for responsible translational use.
